1 |
AFFONSO, C.; ROSSI, A. L. D.; VIEIRA, F. H. A.; CARVALHO,
A. C. P. d. L. F. de. Deep learning for biological image classification.
Expert Systems with Applications, v. 85, p. 114–122, 2017. Disponível em:
<https://doi.org/10.1016/j.eswa.2017.05.039>.
|
|
2 |
AL-GHAFER, I. A.; ALAFESHAT, N.; ALSHOMALI, L.; ALANEE, S.;
QATTOUS, H.; AZZEH, M.; ALKHATEEB, A. Nmf-guided feature selection and
genetic algorithm-driven framework for tumor mutational burden classification
in bladder cancer using multi-omics data. Network Modeling Analysis in Health
Informatics and Bioinformatics, v. 13, n. 1, p. 26, 5 2024. ISSN 2192-6670.
Disponível em: <https://doi.org/10.1007/s13721-024-00460-7>.
|
|
3 |
ALFRED, R.; LIM, Y.; HAVILUDDIN, H.; ON, C. K. Computational science and
technology: 6th iccst 2019, kota kinabalu, malaysia. In: Lecture Notes in Electrical
Engineering. [S.l.]: Springer, 2020. v. 603.
|
|
4 |
ALICKOVIC, E.; SUBASI, A. Breast cancer diagnosis using ga feature selection
and rotation forest. Neural Computing and Applications, Springer, v. 28, n. 4,
p. 753–763, 2017. Disponível em: <https://link.springer.com/article/10.1007/
s00521-015-2103-9>.
|
|
5 |
ANCONA, A.; CERQUETI, R.; VAGNANI, G. A novel methodology to
disambiguate organization names: an application to eu framework programmes data.
Scientometrics, Springer, v. 128, n. 8, p. 4447–4474, 2023. Disponível em: <https:
//ideas.repec.org/a/spr/scient/v128y2023i8d10.1007_s11192-023-04746-x.html>.
|
|
6 |
ANG, J.; MIRZAL, A.; HARON, H.; HAMED, H. Supervised, unsupervised,
and semi-supervised feature selection: A review on gene selection. IEEE/ACM
Transactions on Computational Biology and Bioinformatics, IEEE, v. 13, n. 5, p.
971–989, 2016.
|
|
7 |
ANTONELLI, A.; FRY, C.; SMITH, R. J.; EDEN, J.; GOVAERTS, R. H. A.;
KERSEY, P.; LUGHADHA, E. N.; ZUNTINI, A. R. State of the world’s plants
and fungi 2023. Royal Botanic Gardens, Kew, 2023.
|
|
8 |
ASHRAF, H.; SAEED, S.; KHAN, S. Optimal feature-centric
approach for eeg-based human emotion identification. IEEE on
Advancements in Artificial Intelligence, IEEE, 2024. Disponível
em: <https://www.researchgate.net/profile/Sheharyar-Khan-12/
publication/379153065_Optimal_Feature-Centric_Approach_for_
EEG-Based_Human_Emotion_Identification/links/65fd2c97d3a08551423d36bc/
Optimal-Feature-Centric-Approach-for-EEG-Based-Human-Emotion-Identification.
pdf>.
|
|
9 |
BAEZA-YATES, R.; RIBEIRO-NETO, B. Modern Information Retrieval. 2nd. ed.
USA: Addison-Wesley Publishing Company, 2008. ISBN 9780321416919.
|
|
10 |
BARBIERI, M. C.; GRISCI, B. I.; DORN, M. Analysis and comparison of
feature selection methods towards performance and stability. Expert Systems
with Applications, v. 249, p. 123667, 2024. ISSN 0957-4174. Disponível em:
<https://www.sciencedirect.com/science/article/pii/S0957417424005335>.
|
|
11 |
BEBBER, D.; CARINE, M.; WOOD, J.; WORTLEY, A.; HARRIS, D.; PRANCE,
G.; DAVIDSE, G.; AL. et. Herbaria are a major frontier for species discovery.
Proceedings of the National Academy of Sciences, v. 107, p. 22169–22171, 2010.
|
|
12 |
BENOS, L.; TAGARAKIS, A. C.; DOLIAS, G.; BERRUTO, R.; KATERIS, D.
Machine learning in agriculture: A comprehensive updated review. Sensors, v. 21,
n. 11, 2021. Disponível em: <https://www.mdpi.com/1424-8220/21/11/3758/
pdf>.
|
|
13 |
BOLóN-CANEDO, V.; SáNCHEZ-MAROñO, N. et al. Feature selection for
high-dimensional data. Progress in Artificial Intelligence, Springer, v. 8, n. 2, p.
93–110, 2019.
|
|
14 |
BONIDIA, R. et al. A novel decomposing model with evolutionary algorithms
for feature selection in long non-coding rnas. IEEE Access, IEEE, v. 8, p.
181683–181697, 2020.
|
|
15 |
BOUREL, M.; SEGURA, A. Multiclass classification methods in ecology. Ecological
Indicators, Elsevier, v. 85, p. 1012–1021, 2018.
|
|
16 |
CAO, Q.; XIAO, X.; BIN, Y.; ZHAO, J.; ZHENG, C. Predpvp: A stacking model
for predicting phage virion proteins based on feature selection methods. Current
Bioinformatics, Bentham Science Publishers, 2024.
|
|
17 |
CHAKI, J.; DEY, N. Pattern analysis of genetics and genomics: a survey of
the state-of-art. Multimedia Tools and Applications, Springer, v. 79, n. 15, p.
11163–11194, 2020.
|
|
18 |
CHANDRASHEKAR, G.; SAHIN, F. A survey on feature selection methods.
Computers and Electrical Engineering, v. 40, n. 1, p. 16–28, 2014.
|
|
19 |
CHATTERJEE, S.; DAS, A. An ensemble algorithm integrating consensusclustering with feature weighting based ranking and probabilistic fuzzy
logic-multilayer perceptron classifier for diagnosis and staging of breast cancer
using heterogeneous datasets. Applied Intelligence, Springer, 2022.
|
|
20 |
CHEEK, M.; LUGHADHA, E. N.; KIRK, P.; LINDON, H.; CARRETERO,
J.; LOONEY, B.; DOUGLAS, B.; HAELEWATERS, D.; GAYA, E.;
LLEWELLYN, T.; AINSWORTH, A. M.; GAFFOROV, Y.; HYDE, K.;
CROUS, P.; HUGHES, M.; WALKER, B. E.; FORZZA, R. C.; WONG,
K. M.; NISKANEN, T. New scientific discoveries: Plants and fungi.
PLANTS, PEOPLE, PLANET, v. 2, n. 5, p. 371–388, 2020. Disponível em:
<https://nph.onlinelibrary.wiley.com/doi/abs/10.1002/ppp3.10148>.
|
|
21 |
CHEN, R.-C.; DEWI, C.; HUANG, S.-W.; CARAKA, R. E. Selecting
critical features for data classification based on machine learning methods.
Journal of Big Data, Springer, v. 7, p. 52, 2020. Disponível em: <https:
//doi.org/10.1186/s40537-020-00327-4>.
|
|
22 |
CHEN, R.-C.; DEWI, C.; HUANG, S.-W.; CARAKA, R. E. Selecting
critical features for data classification based on machine learning methods.
Journal of Big Data, Springer, v. 7, n. 1, p. 52, 2020. Disponível em:
<https://doi.org/10.1186/s40537-020-00327-4>.
|
|
23 |
CHEN, Y.; MA, L.; YU, D.; ZHANG, H.; FENG, K.; WANG, X. Comparison of
feature selection methods for mapping soil organic matter in subtropical restored
forests. Ecological Indicators, Elsevier, v. 137, p. 108718, 2022. Disponível em:
<https://www.sciencedi>.
|
|
24 |
CHRISTEN, P.; CHRISTEN, P. Evaluation of matching quality and complexity.
Data Matching: Concepts and Techniques for Record Linkage, Entity
Resolution, and Duplicate Detection, Springer, p. 163–184, 2012. Disponível em:
<https://link.springer.com/book/10.1007/978-3-642-31164-2>.
|
|
25 |
COGATO, A.; PINNA, D.; PEZZUOLO, A.; PORNARO, C. Applications of
satellite platforms and machine learning for mapping and monitoring grasslands
and pastures: A systematic and comprehensive review. Smart Agricultural
Technologies, 2024. Disponível em: <https://www.sciencedirect.com/science/
article/pii/S277237552400176X>.
|
|
26 |
COLLI-SILVA, M.; PIRANI, J. R. Biogeographic patterns of galipeinae (galipeeae,
rutaceae) in brazil: species richness and endemism at different latitudes of the
atlantic forest “hotspot”. Flora, Elsevier, v. 251, p. 77–87, 2019.
|
|
27 |
COOPER, L.; LAPORTE, M. A. e. a. The planteome database: An integrated
resource for reference ontologies, plant genomics, and phenomics. Nucleic Acids
Research, 2018. Disponível em: <https://academic.oup.com/nar/article-abstract/
46/D1/D1168/4653531>.
|
|
28 |
CUTLER, D. R.; JR., T. C. E.; BEARD, K. H.; CUTLER, A.; HESS, K. T.;
GIBSON, J.; LAWLER, J. J. Random forests for classification in ecology. Ecology,
Ecological Society of America, v. 88, n. 11, p. 2783–2792, 2007.
|
|
29 |
DROUSIOTIS, E.; JOYCE, D.; VARSI, A.; SPIRAKIS, P.; MASKELL, S.
Intrinsically interpretable decision trees for healthcare applications. ResearchSquare,
2024. Disponível em: <https://www.researchsquare.com/article/rs-4608203/
latest>.
|
|
30 |
EFFROSYNIDIS, D.; ARAMPATZIS, A. An evaluation of feature selection
methods for environmental data. Ecological Informatics, Elsevier, v. 61, p. 101224,
2021.
|
|
31 |
ELMAGARMID, A. K.; IPEIROTIS, P. G.; VERYKIOS, V. S. Duplicate record
detection: A survey. IEEE Transactions on knowledge and data engineering, IEEE,
v. 19, n. 1, p. 1–16, 2006.
|
|
32 |
ENDARA, L.; COLE, H.; BURLEIGH, J.; NAGALINGUM, N.; BOGUNOVIć
2015). ACKLIN, J. MSome of these feature selection techniques can also play a
role in classification models J. B.; LIU, J.; RANADE, S.; CUI, H. Building the
“plant glossary”—a controlled botanical vocabulary using terms extracted from the
floras of north america and china. Taxon, v. 66, 08 2017.
|
|
33 |
FLACH, P.; KULL, M. Precision-recall-gain curves: Pr analysis done right. In:
Advances in Neural Information Processing Systems (NeurIPS). [s.n.], 2015.
p. 837–845. Disponível em: <https://proceedings.neurips.cc/paper/2015/file/
33e8075e9970de0cfea955afd4644bb2-Paper.pdf>.
|
|
34 |
FONTAINE, B.; PERRARD, A.; BOUCHET, P. 21 years of shelf life between
discovery and description of new species. Current Biology, v. 22, p. R943–R944,
2012.
|
|
35 |
FORMAN, G. An extensive empirical study of feature selection metrics for text
classification. Journal of Machine Learning Research, Hewlett-Packard, v. 3, p.
1289–1305, 2003. Disponível em: <https://www.researchgate.net/publication/
248591742_An_extensive_empirical_study_of_feature_selection_metrics_
for_text_classification_J>.
|
|
36 |
FREITAS, A. A. Comprehensible classification models: a position paper. SIGKDD
Explorations, v. 15, n. 1, p. 1–10, 2014.
|
|
37 |
GAJJAR, V. K.; NAMBISAN, A. K.; KOSBAR, K. L. Plant identification in a
combined-imbalanced leaf dataset. IEEE Access, v. 10, p. 37882–37892, 2022.
|
|
38 |
GANCHEVA, V.; STOEV, H. Optimization and performance analysis of cat
method for dna sequence similarity searching and alignment. Preprints, 2024.
|
|
39 |
GAO, J.; LIU, Y.; DUAN, C.; DING, P.; SONG, J. Research on fault
diagnosis of electric gate valve in nuclear power plant based on the vmdmdi-issa-rf model. Annals of Nuclear Energy, Elsevier, 2024. Disponível em:
<https://www.sciencedirect.com/science/article/pii/S0306454924003645>.
|
|
40 |
GARCÍA, S.; LUENGO, J.; HERRERA, F. Data preprocessing in data mining.
[S.l.]: Springer, 2015. v. 72.
|
|
41 |
GASTON, K. Biodiversity and extinction: species and people. Progress in Physical
Geography, v. 29, p. 239–247, 2005.
|
|
42 |
GHARIBI, A.; SEHHATI, M.; VARD, A.; MOHEBIAN, M. Identification of
gene signatures for classifying of breast cancer subtypes using protein interaction
database and support vector machines. In: IEEE. 2015 5th International
Conference on Computer and Knowledge Engineering (ICCKE). [S.l.], 2015. p.
195–200.
|
|
43 |
GOMAA, W. H.; FAHMY, A. A. A survey of text similarity approaches.
International Journal of Computer Applications, Foundation of Computer
Science (FCS), NY, USA, v. 68, n. 13, p. 13–18, 2013. Disponível em:
<https://research.ijcaonline.org/volume68/number13/pxc3887118.pdf>.
|
|
44 |
GOODWIN, Z.; HARRIS, D.; FILER, D.; WOOD, J.; SCOTLAND, R.
Widespread mistaken identity in tropical plant collections. Current Biology, v. 25,
p. R1066–R1067, 2015.
|
|
45 |
GOVAERTS, R.; LUGHADHA, E. N.; BLACK, N.; TURNER, R. e. a.
The world checklist of vascular plants, a continuously updated resource
for exploring global plant diversity. Scientific Data, 2021. Disponível em:
<https://www.nature.com/articles/s41597-021-00997-6.pdf>.
|
|
46 |
GRANDINI, M.; BAGLI, E.; VISANI, G. Metrics for multi-class classification:
an overview. arXiv preprint arXiv:2008.05756, 2020. Disponível em:
<https://arxiv.org/pdf/2008.05756>.
|
|
47 |
GYAWALI, B.; ANASTASIOU, L.; KNOTH, P. Deduplication of scholarly
documents using locality sensitive hashing and word embeddings. In: EUROPEAN
LANGUAGE RESOURCES ASSOCIATION (ELRA). Proceedings of the 12th
Conference on Language Resources and Evaluation (LREC 2020). Marseille, France,
2020. p. 901–910. Disponível em: <https://aclanthology.org/2020.lrec-1.113.pdf>.
|
|
48 |
HE, H.; MA, Y. Imbalanced Learning: Foundations, Algorithms, and Applications.
[S.l.]: IEEE Transactions on Knowledge and Data Engineering, 2022. v. 34.
|
|
49 |
HIRA, Z.; GILLIES, D. A review of feature selection and feature extraction
methods applied on microarray data. Advances in Bioinformatics, Hindawi,
v. 2015, p. 198363, 2015. Disponível em: <https://onlinelibrary.wiley.com/doi/
abs/10.1155/2015/198363>.
|
|
50 |
HORN, G. V.; AODHA, O. M.; SONG, Y.; CUI, Y.; SUN, C.; SHEPARD, A.;
ADAM, H.; PERONA, P.; BELONGIE, S. The iNaturalist Species Classification
and Detection Dataset. 2018. Disponível em: <https://arxiv.org/abs/1707.06642>.
|
|
51 |
HORTAL, J.; BELLO, F.; DINIZ-FILHO, J.; LEWINSOHN, T.; LOBO, J.;
LADLE, R. Seven shortfalls that beset large-scale knowledge of biodiversity.
Annual Review of Ecology, Evolution, and Systematics, v. 46, p. 523–549, 2015.
|
|
52 |
HOU, Y.; DAI, Y. Spatial configuration and sustainable conservation
of ecotourism resources in the dabie mountains, eastern china, using an
ecosystem services model. Diversity, v. 16, n. 12, p. 782, 2024. Disponível em:
<https://www.mdpi.com/1424-2818/16/12/782>.
|
|
53 |
HUANG, S.; LI, Q.; WANG, L.; WANG, Y. Score-based causal feature selection
for cancer risk prediction. In: 2023 IEEE International Conference on Machine
Learning. IEEE, 2023. Disponível em: <https://ieeexplore.ieee.org/abstract/
document/10219672/>.
|
|
54 |
IYER, S. G.; BANERJEE, A. K.; BHOWMICK, A. R. Making choices that matter
– use of statistical regularization in species distribution modelling for identification
of climatic indicators – a case study with mikania micrantha kunth in india.
Ecological Indicators, Elsevier, v. 98, p. 92–103, 2019.
|
|
55 |
JANGID, A. K.; SHA, A. A.; THAKKAR, S.; CHAWLA, N.; MV, B.;
SHARP, T.; SATYANARAYAN, K.; SESHAMANI, G. Bear biometrics:
developing an individual recognition technique for sloth bears. Mammalian
Biology, Springer, p. 1–9, 2024. Disponível em: <https://www.researchgate.
net/publication/378233518_Bear_biometrics_developing_an_individual_
recognition_technique_for_sloth_bears>.
|
|
56 |
JIANG, Y.; LI, C. mrmr-based feature selection for classification of
cotton foreign matter using hyperspectral imaging. Computers and
Electronics in Agriculture, Elsevier, v. 119, p. 191–200, 2015. Disponível em:
<https://doi.org/10.1016/j.compag.2015.10.017>.
|
|
57 |
JOHNSON, J.; KHOSHGOFTAAR, T. Survey on deep learning with class
imbalance. Journal of Big Data, v. 6, n. 1, p. 1–25, 2019.
|
|
58 |
JOVANOVIC, M.; RADOVANOVIC, S.; VUKICEVIC, M.; POUCKE, S. V.;
DELIBASIC, B. Building interpretable predictive models for pediatric hospital
readmission using tree-lasso logistic regression. Artificial Intelligence in Medicine,
Elsevier, v. 72, p. 12–21, 2016.
|
|
59 |
JOVIć, A.; BRKIć, K.; BOGUNOVIć, N. A review of feature selection methods
with applications. In: 2015 38th International Convention on Information and
Communication Technology, Electronics and Microelectronics (MIPRO). [S.l.: s.n.],
2015. p. 1200–1205.
|
|
60 |
JR, L. G.; SIRACUSA, P. de. A survey of biodiversity informatics: Concepts,
practices, and challenges. Wiley Interdisciplinary Reviews: Data Mining
and Knowledge Discovery, Wiley, v. 11, n. 2, p. e1394, 2021. Disponível em:
<https://wires.onlinelibrary.wiley.com/doi/abs/10.1002/widm.1394>.
|
|
61 |
KAFRAWY, P.; FATHI, H.; QARAAD, M.; KELANY, A.; CHEN, X. An efficient
svm-based feature selection model for cancer classification using high-dimensional
microarray data. IEEE Access, IEEE, v. 9, p. 155353–155369, 2021.
|
|
62 |
KIRASICH, K.; SMITH, T.; SADLER, B. Random forest vs logistic regression:
Binary classification for heterogeneous datasets. SMU Data Science Review, v. 1,
n. 3, p. Article 9, 2018.
|
|
63 |
KRUK, C.; DEVERCELLI, M.; HUSZAR, V. L. M.; HERNáNDEZ, E.; BEAMUD,
G.; DIAZ, M.; SILVA, L. H. S.; SEGURA, A. M. Classification of reynolds
phytoplankton functional groups using individual traits and machine learning
techniques. Freshwater Biology, John Wiley Sons Ltd, v. 62, n. 10, p. 1681–1692,
2017. Disponível em: <https://onlinelibrary.wiley.com/doi/10.1111/fwb.12968>.
|
|
64 |
KUMAR, A.; SINGH, I.; KASHYAP, M.; KUMAR, A.; DEVI,
N. B. Integration of machine learning and remote sensing in crop
yield prediction: A review. ResearchGate, 2024. Disponível em:
<https://www.researchgate.net/profile/Anil-Kumar-833/publication/
388674945_Integration_of_machine_learning_and_remote_sensing_
in_crop_yield_prediction_A_review/links/67a1eca4207c0c20fa76de52/
Integration-of-machine-learning-and-remote-sensing-in-crop-yield-prediction-A-review.
pdf>.
|
|
65 |
LAVNER, Y.; PÉREZ-GRANADOS, C. Computational bioacoustics and
automated recognition of bird vocalizations: new tools, applications, and methods
for bird monitoring. Frontiers in Bird Science, v. 16, n. 12, 2024. Disponível em:
<https://www.frontiersin.org/journals/bird-science/articles/10.3389/fbirs.2024.
1518077/full>.
|
|
66 |
LEE, S. H.; CHAN, C. S.; MAYO, S. J.; REMAGNINO, P. How
deep learning extracts and learns leaf features for plant classification.
Pattern Recognition, Elsevier, v. 71, p. 1–13, 2017. Disponível em:
<https://doi.org/10.1016/j.patcog.2017.05.015>.
|
|
67 |
LEE, T. Y. Plantpan3.0: A new and updated resource for reconstructing
transcriptional regulatory networks from chip-seq experiments in plants.
Nucleic Acids Research, 2019. Disponível em: <https://academic.oup.com/nar/
article-abstract/47/D1/D1155/5160978>.
|
|
68 |
LETOVSKY, S. I. et al. Gdb: The human genome database. Nucleic Acids
Research, Oxford University Press, v. 26, n. 1, p. 94–99, 1998.
|
|
69 |
LI, J.; CHENG, K.; WANG, S.; MORSTATTER, F.; TREVINO, R. P.; TANG, J.;
LIU, H. Feature selection: A data perspective. ACM Computing Surveys, ACM,
v. 50, n. 6, p. 94:1–94:45, 2017. Disponível em: <https://doi.org/10.1145/3136625>.
|
|
70 |
LIANG, C.; SUN, Q.; LI, J.; JI, B.; WU, W.; ZHANG, F. An interpretable ensemble
trees method with joint analysis of static and dynamic features for myocardial
infarction detection. Physiological Measurement, IOP Science, 2024. Disponível em:
<https://iopscience.iop.org/article/10.1088/1361-6579/ad6529/meta>.
|
|
71 |
LIU, H.; YIN, Q.; WANG, W. Y. Towards explainable nlp: A generative
explanation framework for text classification. arXiv preprint, v. 1811.00196v2,
2019. Disponível em: <https://arxiv.org/abs/1811.00196>.
|
|
72 |
LIU, S.; XU, C.; ZHANG, Y.; LIU, J.; YU, B.; LIU, X.; DEHMER, M. Feature
selection of gene expression data for cancer classification using double rbf-kernels.
BMC Bioinformatics, Springer, v. 19, n. 1, p. 396, 2018.
|
|
73 |
LUQUE, A.; CARRASCO, A.; MARTíN, A.; HERAS, A. de L. The impact of
class imbalance in classification performance metrics based on the binary confusion
matrix. Pattern Recognition, v. 91, p. 216–231, 2019.
|
|
74 |
MAITNER, B.; GALLAGHER, R.; SVENNING, J.-C.; TIETJE, M.; WENK, E.;
EISERHARDT, W. A global assessment of the raunkiæran shortfall in plants:
Geographic biases in our knowledge of plant traits. The New Phytologist, 2023.
|
|
75 |
MAITNER, B. S.; BOYLE, B.; CASLER, N.; CONDIT, R. e. a. The bien r
package: A tool to access the botanical information and ecology network (bien)
database. Methods in Ecology and Evolution, 2018. Disponível em: <https:
//besjournals.onlinelibrary.wiley.com/doi/abs/10.1111/2041-210X.12861>.
|
|
76 |
MALLAH, S.; KHAKI, B. D.; DAVATGAR, N.; SCHOLTEN, T.; AMIRIANCHAKAN, A.; EMADI, M.; KERRY, R.; MOSAVI, A. H.; TAGHIZADEHMEHRJARDI, R. Predicting soil textural classes using random forest models:
Learning from imbalanced dataset. Agronomy, MDPI, v. 12, p. 2613, 2022.
|
|
77 |
MANZOOR, M.; FAROOQ, M.; ABID, A. Stylometry-driven framework
for urdu intrinsic plagiarism detection: a comprehensive analysis using
machine learning, deep learning, and large language models. Neural
Computing and Applications, Springer, 2025. Disponível em: <https:
//link.springer.com/article/10.1007/s00521-024-10966-w>.
|
|
78 |
MARQUES, A.; LAMAS, C. Taxonomia zoológica no brasil: estado da arte,
expectativas e sugestões de ações futuras. Papéis Avulsos de Zoologia (São Paulo),
v. 46, p. 139–174, 12 2005.
|
|
79 |
MCELHINNEY, J.; CATACUTAN, M.; MAWART, A. Interfacing machine
learning and microbial omics: a promising means to address environmental
challenges. Frontiers in Microbiology, Frontiers, v. 13, p. 851450, 2022. Disponível
em: <https://www.frontiersin.org/articles/10.3389/fmicb.2022.851450/full>.
|
|
80 |
MCINTYRE, N.; BULOVIC, N.; KEMANGA, B. Hydrological classification of
mine pit lakes using modeling experiments. Journal of Environmental Management,
v. 341, p. 117755, 2024. Disponível em: <https://www.sciencedirect.com/science/
article/pii/S0301479724030433>.
|
|
81 |
MIAO, J.; NIU, L. A survey on feature selection. Procedia Computer
Science, Elsevier, v. 91, p. 919–926, 2016. Disponível em: <https:
//doi.org/10.1016/j.procs.2016.07.111>.
|
|
82 |
MIRZAEI, S.; PARSAFARD, P. Text classification based on discriminativesemantic features and variance of fuzzy similarity. International Journal of
Intelligent Systems and Applications, MECS, v. 2022, n. 2, p. 26–39, 2022.
Disponível em: <https://www.researchgate.net/publication/360301091>.
|
|
83 |
MOCK, F.; KRETSCHMER, F.; KRIESE, A.; BÖCKER, S.; MARZ, M.
Taxonomic classification of dna sequences beyond sequence similarity using deep
neural networks. Proceedings of the National Academy of Sciences, v. 119, n. 31, p.
e2122636119, 2022.
|
|
84 |
MYERS, N.; MITTERMEIER, R.; MITTERMEIER, C.; FONSECA, G.; KENT,
J. Biodiversity hotspots for conservation priorities. Nature, v. 403, p. 853–858,
2000.
|
|
85 |
NAVARRO, G. A guided tour to approximate string matching. ACM computing
surveys (CSUR), ACM New York, NY, USA, v. 33, n. 1, p. 31–88, 2001.
|
|
86 |
NEUMANN, K. International code for phytolith nomenclature (icpn) 2.0. Annals
of Botany, 2019. Disponível em: <https://academic.oup.com/aob/article-abstract/
124/2/189/5537002>.
|
|
87 |
OPARA, I. K.; OPARA, U. L.; OKOLIE, J. A.; FAWOLE, O. A. Machine
learning application in horticulture and prospects for predicting fresh
produce losses and waste: A review. Plants, v. 13, n. 9, 2024. Disponível em:
<https://www.mdpi.com/2223-7747/13/9/1200/pdf>.
|
|
88 |
OSTROSKI, P.; SAITER, F. Z.; AMORIM, A. M.; FIASCHI, P. Angiosperm
endemism in a brazilian atlantic forest biodiversity hot-point. Brazilian Journal of
Botany, 2020. Disponível em: <https://doi.org/10.1007/s40415-020-00603-w>.
|
|
89 |
PACIFICO, L. D. S.; MACARIO, V.; OLIVEIRA, J. F. L. Plant classification
using artificial neural networks. 2018 International Joint Conference on Neural
Networks (IJCNN), IEEE, p. 1–8, 2018.
|
|
90 |
PANDEY, C.; SETHY, P. K.; BEHERA, S. K.; VISHWAKARMA, J. Smart agriculture: Technological advancements on agriculture—a systematical review. Elsevier,
2022. Disponível em: <https://www.researchgate.net/profile/Prabira-Sethy/
publication/357702838_Smart_agriculture_Technological_advancements_
on_agriculture-_A_systematical_review/links/61dc21af3a192d2c8aee049a/
Smart-agriculture-Technological-advancements-on-agriculture-A-systematical-review.
pdf>.
|
|
91 |
PASHAEI, E.; AYDIN, N. Binary black hole algorithm for feature selection and
classification on biological data. Applied Soft Computing, v. 56, 03 2017.
|
|
92 |
PASHAEI, E.; PASHAEI, E. Hybrid binary arithmetic optimization algorithm
with simulated annealing for feature selection in high-dimensional biomedical data.
Journal of Supercomputing, Springer, v. 78, n. 14, p. 15598–15637, 2022.
|
|
93 |
PATANKAR, M.; CHAURASIA, V.; SHANDILYA, M. A novel densenet deep neural network with enhanced feature selection method for
classification of different stages of tuberculosis using chest x-ray images.
Multimedia Tools and Applications, Springer, 2024. Disponível em: <https:
//link.springer.com/article/10.1007/s11042-024-20381-x>.
|
|
94 |
PETRIE, S. M.; JULIUS, T. D. A novel text representation which enables image
classifiers to also simultaneously classify text, applied to name disambiguation.
Scientometrics, Springer, p. 1–25, 2023.
|
|
95 |
PINHEIRO, T. M. Taxonomia zoológica no brasil. Dissertação de Mestrado,
UNIVERSIDADE FEDERAL DO RIO DE JANEIRO - UFRJ, 2017.
|
|
96 |
PRAKOSO, D. et al. Short text similarity measurement methods: A review.
Journal of Big Data and Analytics in Practice, Springer, v. 3, n. 1, p. 33–44, 2021.
Disponível em: <https://link.springer.com/article/10.1007/s00500-020-05479-2>.
|
|
97 |
PUDJIHARTONO et al. A review of feature selection methods for machine
learning-based disease risk prediction. Frontiers in Bioinformatics, Frontiers, v. 2,
p. 1–16, 2022.
|
|
98 |
RADHAKRISHNAN, N.; PILLAI, A. S. Comparison of water quality classification
models using machine learning. Proceedings of the International Conference on
Electronics and Communication Systems, v. 1, p. 1183–1187, 2020.
|
|
99 |
REMESEIRO, B.; BOLON-CANEDO, V. A review of feature selection methods
in medical applications. Computers in Biology and Medicine, Elsevier, v. 112,
p. 103375, 2019. Disponível em: <https://doi.org/10.1016/j.compbiomed.2019.
103375>.
|
|
100 |
ROBERT, V.; SZOKE, S.; JABAS, B.; VU, D.; CHOUCHEN, O.; BLOM, E.;
CARDINALI, G. Biolomics software: Biological data management, identification,
classification, and statistics. The Open Applied Informatics Journal, v. 5, p. 87–98,
2011.
|
|
101 |
RODRIGUEZ, J. D.; PEREZ, A.; LOZANO, J. A. A framework for wrapper
feature selection in supervised classification. Pattern Recognition, v. 86, p. 181–197,
2019.
|
|
102 |
ROHART, F.; GAUTIER, B.; SINGH, A. mixomics: An r package for ’omics
feature selection and multiple data integration. PLoS Computational Biology, Public
Library of Science (PLoS), v. 13, n. 11, p. e1005752, 2017. Disponível em: <https:
//journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1005752>.
|
|
103 |
SAEYS, Y. et al. A review of feature selection techniques in bioinformatics.
Bioinformatics, Oxford University Press, v. 23, n. 19, p. 2507–2517, 2007.
|
|
104 |
SAJJADI, M.; BACHEM, O.; LUCIC, M. Assessing generative models via
precision and recall. In: Advances in Neural Information Processing Systems
(NeurIPS). [s.n.], 2018. p. 5220–5230. Disponível em: <https://proceedings.
neurips.cc/paper/2018/file/f7696a9b362ac5a51c3dc8f098b73923-Paper.pdf>.
|
|
105 |
SCHRATZ, P.; MUENCHOW, J.; ITURRITXA, E. et al. Monitoring forest health
using hyperspectral imagery: Does feature selection improve the performance of
machine-learning techniques? Remote Sensing, MDPI, v. 13, n. 23, p. 4832, 2021.
|
|
106 |
SHARIF, M.; KHAN, M.; IQBAL, Z.; AZAM, M.; LALI, M. Detection
and classification of citrus diseases in agriculture based on optimized
weighted segmentation and feature selection. Computers and Electronics
in Agriculture, Elsevier, v. 150, p. 220–234, 2018. Disponível em: <https:
//www.sciencedirect.com/science/article/pii/S0168169917306373>.
|
|
107 |
SHARMA, A.; SINGH, V. Machine learning-based feature extraction techniques
for epileptic seizure detection using eeg bio-signals. Naturalista Campano, Museo
Naturalistico, 2024. Disponível em: <https://www.museonaturalistico.it/index.
php/journal/article/view/259>.
|
|
108 |
SHARMA, V.; RESTREPO, M. I.; SARKAR, I. N. Solr-plant: efficient extraction
of plant names from text. BMC Bioinformatics, v. 20, n. 1, p. 263, 2019. Disponível
em: <https://doi.org/10.1186/s12859-019-2874-6>.
|
|
109 |
SHUKLA, A. K. Multi-population adaptive genetic algorithm for selection
of microarray biomarkers. Neural Computing and Applications, v. 32,
n. 15, p. 11897–11918, 8 2020. ISSN 1433-3058. Disponível em: <https:
//doi.org/10.1007/s00521-019-04671-2>.
|
|
110 |
SIMON, S. M.; GLAUM, P.; VALDOVINOS, F. S. Interpreting random
forest analysis of ecological models to move from prediction to explanation.
Scientific Reports, v. 13, p. 3881, 2023. Disponível em: <https://doi.org/10.1038/
s41598-023-30313-8>.
|
|
111 |
SINHA, R.; CHAWLA, V.; PALWE, S.; SINGH, O. Prediction of chronic
respiratory diseases using machine learning algorithms. In: Lecture
Notes on Computing and Applications. Springer, 2024. Disponível em:
<https://link.springer.com/chapter/10.1007/978-981-97-7571-2_2>.
|
|
112 |
SLENTER, D. N.; KUTMON, M.; HANSPERS, K. e. a. Wikipathways: A
multifaceted pathway database bridging metabolomics to other omics research.
Nucleic Acids Research, 2018. Disponível em: <https://academic.oup.com/nar/
article-abstract/46/D1/D661/4612963>.
|
|
113 |
SMITH, T. F.; WATERMAN, M. S. et al. Identification of common molecular
subsequences. Journal of molecular biology, Elsevier Science, v. 147, n. 1, p.
195–197, 1981.
|
|
114 |
SOLORIO-FERNáNDEZ, S.; CARRASCO-OCHOA, J. A.; MARTíNEZTRINIDAD, J. F. A review of unsupervised feature selection methods. Artificial
Intelligence Review, v. 53, p. 907–948, 2018.
|
|
115 |
SONG, G.; WANG, Q. Species classification from hyperspectral leaf information
using machine learning approaches. Ecological Informatics, Elsevier, v. 76, p.
102141, 2023.
|
|
116 |
STRODTHOFF, N.; WAGNER, P.; WENZEL, M.; SAMEK, W. Udsmprot:
universal deep sequence models for protein classification. Bioinformatics, v. 36,
n. 8, p. 2401–2409, 2020.
|
|
117 |
SUBANYA, B.; RAJALAXMI, R. R. Feature selection using artificial bee colony
for cardiovascular disease classification. Proceedings of the International Conference
on Electronics and Communication Systems, v. 1, p. 104–110, 2014.
|
|
118 |
TADIST et al. Feature selection methods and genomic big data: A systematic
review. Journal of Big Data, Springer, v. 6, n. 1, p. 79, 2019.
|
|
119 |
TANG, J.; ALELYANI, S.; LIU, H. Feature selection for classification: A review.
Data Classification: Algorithms and Applications, CRC Press, 2014. Disponível em:
<https://www.math.chalmers.se/Stat/Grundutb/GU/MSA220/S18/featselect.
pdf>.
|
|
120 |
TANG, J.; WANG, Y.; FU, J.; ZHOU, Y.; LUO, Y. A critical assessment of the
feature selection methods used for biomarker discovery in current metaproteomics
studies. Briefings in Bioinformatics, Oxford University Press, v. 21, n. 4, p.
1378–1390, 2020.
|
|
121 |
TANG, X.; SHI, Z.; JIN, M. Multi-category multi-state information ensemble-based
classification method for precise diagnosis of three cancers. Neural Computing and
Applications, Springer, v. 33, n. 14, p. 15901–15917, 2021.
|
|
122 |
TIAN, F. e. a. Noncodev6: An updated database dedicated to long non-coding rna
annotation in both animals and plants. Nucleic Acids Research, 2020. Disponível
em: <https://academic.oup.com/nar/article-abstract/49/D1/D165/5983627>.
|
|
123 |
TIRELLI, T.; PESSANI, D. Importance of feature selection in decision-tree and
artificial-neural-network ecological applications. alburnus alburnus alborella: A
practical example. Ecological Informatics, Elsevier, v. 6, p. 309–315, 2011.
|
|
124 |
TOUKACH, P. V.; EGOROVA, K. S. Carbohydrate structure database merged from
bacterial, archaeal, plant and fungal parts. Nucleic Acids Research, 2016. Disponível
em: <https://academic.oup.com/nar/article-abstract/44/D1/D1229/2503128>.
|
|
125 |
TRACY, J. L.; TRABUCCO, A.; LAWING, A. M.; GIERMAKOWSKI, J. T.
Random subset feature selection for ecological niche models of wildfire activity
in western north america. Ecological Modelling, Elsevier, v. 386, p. 19–30,
2018. Disponível em: <https://www.sciencedirect.com/science/article/pii/
S0304380018301868>.
|
|
126 |
TURLAND, N. J.; WIERSEMA, J. H.; BARRIE, F. R.; GREUTER, W.;
HAWKSWORTH, D. L.; HERENDEEN, P. S.; KNAPP, S.; KUSBER, W.-H.; LI,
D.-Z.; MARHOLD, K. et al. International Code of Nomenclature for algae, fungi,
and plants (Shenzhen Code) adopted by the Nineteenth International Botanical
Congress Shenzhen, China, July 2017. [S.l.]: Koeltz botanical books, 2018.
|
|
127 |
Uzma; AL-OBEIDAT, F.; TUBAISHAT, A.; SHAH, B.; HALIM, Z. Gene
encoder: a feature selection technique through unsupervised deep learningbased clustering for large gene expression data. Neural Computing and
Applications, v. 34, n. 11, p. 8309–8331, 6 2022. ISSN 1433-3058. Disponível em:
<https://doi.org/10.1007/s00521-020-05101-4>.
|
|
128 |
VERMA, R. K.; LOKHANDE, K. B.; SRIVASTAVA, P. K.; SINGH, A. Elucidating
b4galnt1 as potential biomarker in hepatocellular carcinoma using machine learning
models and mutational dynamics explored through md simulation. Informatics
in Medicine Unlocked, v. 48, p. 101514, 2024. ISSN 2352-9148. Disponível em:
<https://www.sciencedirect.com/science/article/pii/S2352914824000704>.
|
|
129 |
WAESE, J. e. a. eplant: Visualizing and exploring multiple levels of data for
hypothesis generation in plant biology. The Plant Cell, 2017. Disponível em:
<https://academic.oup.com/plcell/article-abstract/29/8/1806/6100398>.
|
|
130 |
WAN, C.; FREITAS, A. A.; MAGALHãES, J. P. D. Predicting the pro-longevity
or anti-longevity effect of model organism genes with new hierarchical feature
selection methods. IEEE/ACM Transactions on Computational Biology and
Bioinformatics, v. 12, n. 2, p. 262–275, 3 2015. ISSN 1545-5963. Disponível em:
<https://doi.org/10.1109/TCBB.2014.2355218>.
|
|
131 |
WANG, J.; DONG, Y. Measurement of text similarity: A survey. Information,
v. 11, n. 9, 2020. ISSN 2078-2489. Disponível em: <https://www.mdpi.com/
2078-2489/11/9/421>.
|
|
132 |
WANG, L.; WANG, Y.; CHANG, Q. Feature selection methods for big data
bioinformatics: A survey from the search perspective. Methods, Elsevier, v. 111, p.
21–31, 2016.
|
|
133 |
WHITTAKER, R.; ARAúJO, M.; JEPSON, P.; LADLE, R.; WATSON, J.;
WILLIS, K. Conservation biogeography: Assessment and prospect. Diversity and
Distributions, v. 11, p. 3–23, 2005.
|
|
134 |
WILSON, E. Biodiversity research requires more boots on the ground. Nature
Ecology & Evolution, v. 1, p. 1590–1591, 2017.
|
|
135 |
XU, J.; ZHANG, Y.; MIAO, D. Three-way confusion matrix for classification: A
measure driven view. Information Sciences, v. 530, p. 81–98, 2020.
|
|
136 |
XU, S. Bayesian naïve bayes classifiers to text classification. Journal of Information
Science, SAGE Publications, v. 42, n. 5, p. 677–694, 2016.
|
|
137 |
YU, D.; LEE, S. J.; LEE, W. J.; KIM, S. C.; LIM, J.; KWON, S. W. Classification
of spectral data using fused lasso logistic regression. Chemometrics and Intelligent
Laboratory Systems, Elsevier, v. 142, p. 70–77, 2015.
|
|
138 |
YU, Z.; CHEN, H.; YOU, J.; WONG, H.-S.; LIU, J.; LI, L.; HAN, G. Double
selection based semi-supervised clustering ensemble for tumor clustering from
gene expression profiles. IEEE/ACM Transactions on Computational Biology and
Bioinformatics, v. 11, n. 4, p. 727–740, 7 2014. ISSN 1545-5963. Disponível em:
<https://doi.org/10.1109/TCBB.2014.2315996>.
|
|
139 |
ZAFARI, M.; SADEGHI-NIARAKI, A.; CHOI, S. M.; ESMAEILY, A. A
practical model for the evaluation of high school student performance based
on machine learning. Applied Sciences, v. 11, n. 23, 2021. Disponível em:
<https://www.mdpi.com/2076-3417/11/23/11534/pdf>.
|
|
140 |
ZHENG, Y.; PENG, Y.; GAO, Y.; YANG, G.; JIANG, Y.; ZHANG, G.; WANG,
L.; YU, J.; HUANG, Y.; WEI, Z.; LIU, J. Identification and dissection of
prostate cancer grounded on fatty acid metabolism-correlative features for
predicting prognosis and assisting immunotherapy. Computational Biology
and Chemistry, v. 115, p. 108323, 2025. ISSN 1476-9271. Disponível em:
<https://www.sciencedirect.com/science/article/pii/S1476927124003116>.
|
|