Optimization approach for feature selection in multi-label classification

Lim, Hee‐Jung; Lee, Jae-Sung; Kim, Dae‐Won

doi:10.1016/j.patrec.2017.02.004

Cited by 61 publications

(24 citation statements)

References 12 publications

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“…Kumar and Minz (2014) pointed out a number of related works which have focused on comparing several feature selection methods for different domain problems. Others have proposed novel feature selection algorithms featuring filter (Lim, Lee, & Kim, 2017; Wang, Wei, Yang, & Wang, 2017), wrapper (Das, Das, & Ghosh, 2017; Zhang et al, 2016), and embedded (Liu, Huang, Meng, Gong, & Zhang, 2016; Zhu, Zhu, Hu, Zhang, & Zuo, 2017) techniques. However, many of these novel algorithms have been developed based only on one type of selection technique, filter, wrapper, or embedded feature selection processes.…”

Section: Introductionmentioning

confidence: 99%

Ensemble feature selection in medical datasets: Combining filter, wrapper, and embedded feature selection results

et al. 2020

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Feature selection is a process aimed at filtering out unrepresentative features from a given dataset, usually allowing the later data mining and analysis steps to produce better results. However, different feature selection algorithms use different criteria to select representative features, making it difficult to find the best algorithm for different domain datasets. The limitations of single feature selection methods can be overcome by the application of ensemble methods, combining multiple feature selection results. In the literature, feature selection algorithms are classified as filter, wrapper, or embedded techniques. However, to the best of our knowledge, there has been no study focusing on combining these three types of techniques to produce ensemble feature selection. Therefore, the aim here is to answer the question as to which combination of different types of feature selection algorithms offers the best performance for different types of medical data including categorical, numerical, and mixed data types. The experimental results show that a combination of filter (i.e., principal component analysis) and wrapper (i.e., genetic algorithms) techniques by the union method is a better choice, providing relatively high classification accuracy and a reasonably good feature reduction rate.

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Section: Introductionmentioning

confidence: 99%

Ensemble feature selection in medical datasets: Combining filter, wrapper, and embedded feature selection results

et al. 2020

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“…Then, the top n features are selected by sorting on the function values. In our earlier studies [21,22], we proposed feature selection methods for a multi-label dataset. In this work, we first applied the method for the TC problem, and then used other conventional feature selection metrics for TC to model a new term selection method.…”

Section: Proposed Methodsmentioning

confidence: 99%

Generalized Term Similarity for Feature Selection in Text Classification Using Quadratic Programming

Lim

Kim

2020

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The rapid growth of Internet technologies has led to an enormous increase in the number of electronic documents used worldwide. To organize and manage big data for unstructured documents effectively and efficiently, text categorization has been employed in recent decades. To conduct text categorization tasks, documents are usually represented using the bag-of-words model, owing to its simplicity. In this representation for text classification, feature selection becomes an essential method because all terms in the vocabulary induce enormous feature space corresponding to the documents. In this paper, we propose a new feature selection method that considers term similarity to avoid the selection of redundant terms. Term similarity is measured using a general method such as mutual information, and serves as a second measure in feature selection in addition to term ranking. To consider balance of term ranking and term similarity for feature selection, we use a quadratic programming-based numerical optimization approach. Experimental results demonstrate that considering term similarity is effective and has higher accuracy than conventional methods.

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“…The information-theory-based feature filter methods generally evaluate the importance of features based on the joint entropy between each feature and labels. We selected the information-theoretic multi-label feature filter, namely quadratic programming-based multi-label feature selection [ 39 ], as a filter operator. This is because it has performed effectively in multi-label feature selection problems.…”

Section: Proposed Methodsmentioning

confidence: 99%

Competitive Particle Swarm Optimization for Multi-Category Text Feature Selection

Lee

Park

Kim

et al. 2019

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Multi-label feature selection is an important task for text categorization. This is because it enables learning algorithms to focus on essential features that foreshadow relevant categories, thereby improving the accuracy of text categorization. Recent studies have considered the hybridization of evolutionary feature wrappers and filters to enhance the evolutionary search process. However, the relative effectiveness of feature subset searches of evolutionary and feature filter operators has not been considered. This results in degenerated final feature subsets. In this paper, we propose a novel hybridization approach based on competition between the operators. This enables the proposed algorithm to apply each operator selectively and modify the feature subset according to its relative effectiveness, unlike conventional methods. The experimental results on 16 text datasets verify that the proposed method is superior to conventional methods.

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Optimization approach for feature selection in multi-label classification

Cited by 61 publications

References 12 publications

Ensemble feature selection in medical datasets: Combining filter, wrapper, and embedded feature selection results

Ensemble feature selection in medical datasets: Combining filter, wrapper, and embedded feature selection results

Generalized Term Similarity for Feature Selection in Text Classification Using Quadratic Programming

Competitive Particle Swarm Optimization for Multi-Category Text Feature Selection

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