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Feature Importance in Gradient Boosting Trees with Cross-Validation Feature Selection
Entropy, 2022 Gradient Boosting Machines (GBM) are among the go-to algorithms on tabular data, which produce state-of-the-art results in many prediction tasks. Despite its popularity, the GBM framework suffers from a fundamental flaw in its base learners. Specifically,
Afek Ilay Adler, Amichai Painsky
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Intelligent Feature Selection Methods: A Survey [PDF]
Engineering and Technology Journal, 2021 Consider feature selection is the main in intelligent algorithms and machine learning to select the subset of data to help acquire the optimal solution.
Noor Jameel, Hasanen S. Abdullah
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ACM Computing Surveys, 2017 Feature selection, as a data preprocessing strategy, has been proven to be effective and efficient in preparing data (especially high-dimensional data) for various data-mining and machine-learning problems. The objectives of feature selection include building simpler and more comprehensible models, improving data-mining performance, and preparing clean,
Jundong Li +6 more
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CBFS: high performance feature selection algorithm based on feature clearness. [PDF]
PLoS ONE, 2012 BACKGROUND: The goal of feature selection is to select useful features and simultaneously exclude garbage features from a given dataset for classification purposes.
Minseok Seo, Sejong Oh
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Feature selection for functional data [PDF]
Journal of Multivariate Analysis, 2016 22 pages, 4 ...
Fraiman, Ricardo +2 more
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Structure Preserving Non-negative Feature Self-Representation for Unsupervised Feature Selection
IEEE Access, 2017 Inspired by the importance of self-representation and structure-preserving ability of features, in this paper, we propose a novel unsupervised feature selection algorithm named structure-preserving non-negative feature self-representation (SPNFSR).
Wei Zhou +3 more
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A Feature Selection Algorithm Performance Metric for Comparative Analysis
Algorithms, 2021 This study presents a novel performance metric for feature selection algorithms that is unbiased and can be used for comparative analysis across feature selection problems.
Werner Mostert +2 more
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Online Feature Selection with Group Structure Analysis [PDF]
, 2016 Online selection of dynamic features has attracted intensive interest in recent years. However, existing online feature selection methods evaluate features individually and ignore the underlying structure of feature stream. For instance, in image analysis, features are generated in groups which represent color, texture and other visual information ...
arxiv +1 more source
Deep Feature Selection Using a Novel Complementary Feature Mask [PDF]
arXiv, 2022 Feature selection has drawn much attention over the last decades in machine learning because it can reduce data dimensionality while maintaining the original physical meaning of features, which enables better interpretability than feature extraction. However, most existing feature selection approaches, especially deep-learning-based, often focus on the
arxiv
, 2010 Publisher Summary This chapter discusses techniques for the selection of a subset of features from a larger pool of available features. The techniques include: outlier removal, data normalization, hypothesis testing, the receiver operating characteristic curve, fisher's discriminant ratio and so on.
Konstantinos Koutroumbas +3 more
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