A Survey on Evolutionary Computation Approaches to Feature Selection

Xue, Bing; Zhang, Mengjie; Browne, Will N.; Yao, Xin

doi:10.1109/tevc.2015.2504420

Cited by 1,471 publications

(707 citation statements)

References 233 publications

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“…Chen et al have applied ant colony optimization (ACO) together with rough set theory for feature selection [8]. In particular, PSO, as a popular metaheuristics, has also been widely adopted for feature selection [40]. Chuang et al have developed an improved binary PSO algorithm for feature selection using gene expression data [12].…”

Section: Metaheuristics For Feature Selectionmentioning

confidence: 99%

Feature selection for high-dimensional classification using a competitive swarm optimizer

2016

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When solving many machine learning problems such as classification, there exists a large number of input features. However, not all features are relevant for solving the problem, and sometimes, including irrelevant features may deteriorate the learning performance. Therefore, it is essential to select the most relevant features, which is known as feature selection. Many feature selection algorithms have been developed, including evolutionary algorithms or particle swarm optimization (PSO) algorithms, to find a subset of the most important features for accomplishing a particular machine learning task. However, the traditional PSO does not perform well for large scale optimization problems, which degrades the effectiveness of PSO for feature selection when the number of features dramatically increases. In this paper, we propose to use a very recent PSO variant, known as competitive swarm optimizer (CSO) that was dedicated to large-scale optimization, for solving high-dimensional feature selection problems. In addition, the CSO, which was originally developed for continuous optimization, is adapted to performing feature selection that can be considered as a combinatorial optimization problem. An archive technique is also introduced to reduce computational cost. Experiments on six benchmark datasets demonstrate that compared to the canonical PSO based and a state-of-the-art PSO variant for feature selection, the proposed CSO-based

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Section: Metaheuristics For Feature Selectionmentioning

confidence: 99%

Feature selection for high-dimensional classification using a competitive swarm optimizer

2016

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“…As a notable multilabel feature wrapper study, Zhang et al [12] proposed a multilabel feature selection method based on a genetic algorithm (GA), which is the most common choice in evolutionary feature wrapper studies [28]. Specifically, their method combined instance-and label-based evaluation metrics [39] as a fitness function to determine label dependency.…”

Section: Related Workmentioning

confidence: 99%

“…This typically results in better classification accuracy [11,12]. For this reason, we focus on a multilabel feature wrapper based on an evolutionary search process [28].…”

Section: Introductionmentioning

confidence: 99%

Effective Evolutionary Multilabel Feature Selection under a Budget Constraint

2018

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Multilabel feature selection involves the selection of relevant features from multilabeled datasets, resulting in improved multilabel learning accuracy. Evolutionary search-based multilabel feature selection methods have proved useful for identifying a compact feature subset by successfully improving the accuracy of multilabel classification. However, conventional methods frequently violate budget constraints or result in inefficient searches due to ineffective exploration of important features. In this paper, we present an effective evolutionary search-based feature selection method for multilabel classification with a budget constraint. The proposed method employs a novel exploration operation to enhance the search capabilities of a traditional genetic search, resulting in improved multilabel classification. Empirical studies using 20 real-world datasets demonstrate that the proposed method outperforms conventional multilabel feature selection methods.

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“…Both styles rely on efficient optimization algorithms. Some commonly used algorithms are random search and greedy search (such as step backward or step forward) and evolutionary search (such as genetic algorithms or particle algorithms) [70,71].…”

Section: Curse Of Dimensionalitymentioning

confidence: 99%

Addressing Complexities of Machine Learning in Big Data: Principles, Trends and Challenges from Systematical Perspectives

Wang¹,

Zhao²,

Huang³

et al. 2017

Preprint

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The concept of 'big data' has been widely discussed, and its value has been illuminated throughout a variety of domains. To quickly mine potential values and alleviate the ever-increasing volume of information, machine learning is playing an increasingly important role and faces more challenges than ever. Because few studies exist regarding how to modify machine learning techniques to accommodate big data environments, we provide a comprehensive overview of the history of the evolution of big data, the foundations of machine learning, and the bottlenecks and trends of machine learning in the big data era. More specifically, based on learning principals, we discuss regularization to enhance generalization. The challenges of quality in big data are reduced to the curse of dimensionality, class imbalances, concept drift and label noise, and the underlying reasons and mainstream methodologies to address these challenges are introduced. Learning model development has been driven by domain specifics, dataset complexities, and the presence or absence of human involvement. In this paper, we propose a robust learning paradigm by aggregating the aforementioned factors. Over the next few decades, we believe that these perspectives will lead to novel ideas and encourage more studies aimed at incorporating knowledge and establishing data-driven learning systems that involve both data quality considerations and human interactions.

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A Survey on Evolutionary Computation Approaches to Feature Selection

Cited by 1,471 publications

References 233 publications

Feature selection for high-dimensional classification using a competitive swarm optimizer

Feature selection for high-dimensional classification using a competitive swarm optimizer

Effective Evolutionary Multilabel Feature Selection under a Budget Constraint

Addressing Complexities of Machine Learning in Big Data: Principles, Trends and Challenges from Systematical Perspectives

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