A decomposition-based multi-objective immune algorithm for feature selection in learning to rank

Li, Wangwang; Chai, Zhengyi; Tang, Zengjie

doi:10.1016/j.knosys.2021.107577

Cited by 10 publications

(8 citation statements)

References 30 publications

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“…(b) Number of features selected (NFS): One of the essential aspects when solving the feature selection problem is to increase the performance of the classifiers to the smallest number of features possible. Given this, the number of selected features is an important objective, pursued in [63][64][65][66][67][68][69][70][71][72][73][74].…”

Section: Pure Multi-objective Functionsmentioning

confidence: 99%

“…(d) Accuracy: Defined in Section 4.2.3 and mathematically in Equation ( 22). This objective function was pursued in [74].…”

Section: Pure Multi-objective Functionsmentioning

confidence: 99%

“…Measures the accuracy of the model when it predicts a positive class. This metric has been used in [22,33,35,45,48,51,55,59,74,83,98,101,106,117,129,132,141,149,164,171,[176][177][178]185] and mathematically is defined as follows:…”

Section: Classifier Metricsmentioning

confidence: 99%

“…NDCG is an extension of the discounted cumulative gain metric, normalized to provide a score between 0 and 1, making comparing different rankings or recommendation systems easier. This metric has been used in [74] (e) Correlation: Used in multi-class classification and seeks to determine the correlation between features and classes. This metric has been used in [63] and mathematically is defined as follows: Given a set of features (F) and a set of labels (L), the correlation r f l between the features f ∈ F and the label l ∈ L should be calculated using a predefined measure.…”

Section: Feature Metricsmentioning

confidence: 99%

“…This exemplifies its role as a foundational and versatile approach in addressing complex optimization tasks. Notably, the support vector machine (SVM) follows, with a notable presence in 17.4% of papers [21,32,35,39,40,43,[55][56][57]59,60,72,74,91,106,107,117,147,164,169,171,[174][175][176][177][178]181,182]. The presence of naive Bayes (NB) [33,35,60,94,106,172,[176][177][178]181] and various decision tree classifiers, including Decision Tree Classifier C4.5 (DT C4.5) [30,94] and random forest (RF) [22,34,147,162,167,176], underscores the ongoing significance of interpretable and ensemblebased methodologies.…”

Section: Most Common Classifiersmentioning

confidence: 99%

See 4 more Smart Citations

Feature Selection Problem and Metaheuristics: A Systematic Literature Review about Its Formulation, Evaluation and Applications

Barrera-García,

Cisternas-Caneo,

Crawford

et al. 2023

Biomimetics

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Feature selection is becoming a relevant problem within the field of machine learning. The feature selection problem focuses on the selection of the small, necessary, and sufficient subset of features that represent the general set of features, eliminating redundant and irrelevant information. Given the importance of the topic, in recent years there has been a boom in the study of the problem, generating a large number of related investigations. Given this, this work analyzes 161 articles published between 2019 and 2023 (20 April 2023), emphasizing the formulation of the problem and performance measures, and proposing classifications for the objective functions and evaluation metrics. Furthermore, an in-depth description and analysis of metaheuristics, benchmark datasets, and practical real-world applications are presented. Finally, in light of recent advances, this review paper provides future research opportunities.

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