Optimal feature subset selection using hybrid binary Jaya optimization algorithm for text classification

Thirumoorthy, K.; Muneeswaran, K.

doi:10.1007/s12046-020-01443-w

Cited by 28 publications

(17 citation statements)

References 29 publications

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“…In addition to the classic particle swarm optimization [17][18][19] and genetic algorithm [20], some other novel bionic algorithms have been successfully applied to text feature selection. For example, the cat swarm optimization algorithm [21], artificial fish swarm algorithm [22], the Jaya optimization algorithm [23], the firefly algorithm [24],the grey wolf optimization algorithm [25]and the ant colony algorithm [26]. To solve the problem of Arabic text classification, Chantar et al proposed an enhanced binary gray wolf optimizer (GWO) as a feature selection method [27].…”

Section: Related Workmentioning

confidence: 99%

A Chaotic Antlion Optimization Algorithm for Text Feature Selection

Chen

Zhou

Shi

2022

Int J Comput Intell Syst

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Text classification is one of the important technologies in the field of text data mining. Feature selection, as a key step in processing text classification tasks, is used to process high-dimensional feature sets, which directly affects the final classification performance. At present, the most widely used text feature selection methods in academia are to calculate the importance of each feature for classification through an evaluation function, and then select the most important feature subsets that meet the quantitative requirements in turn. However, ignoring the correlation between the features and the effect of their mutual combination in this way may not guarantee the best classification effect. Therefore, this paper proposes a chaotic antlion feature selection algorithm (CAFSA) to solve this problem. The main contributions include: (1) Propose a chaotic antlion algorithm (CAA) based on quasi-opposition learning mechanism and chaos strategy, and compare it with the other four algorithms on 11 benchmark functions. The algorithm has achieved a higher convergence speed and the highest optimization accuracy. (2) Study the performance of CAFSA using CAA for feature selection when using different learning models, including decision tree, Naive Bayes, and SVM classifier. (3) The performance of CAFSA is compared with that of eight other feature selection methods on three Chinese datasets. The experimental results show that using CAFSA can reduce the number of features and improve the classification accuracy of the classifier, which has a better classification effect than other feature selection methods.

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Section: Related Workmentioning

confidence: 99%

A Chaotic Antlion Optimization Algorithm for Text Feature Selection

Chen

Zhou

Shi

2022

Int J Comput Intell Syst

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“…There are several examples in literature that presents binary JAYA in one way or another. The binary JAYA could be blended with some other transfer functions to achieve the required results based on the applications being tackled [59,62,[82][83][84].…”

Section: Binary Jaya Algorithmmentioning

confidence: 99%

“…The results were better than those produced by other comparative methods. In another study [83], a new binary version of JAYA algorithm is proposed for feature selection problems. Naive Bayes and Support Vector Machine are used to evaluate the resulting feature subsets using different text corpus data sets.…”

Section: Initial Populationmentioning

confidence: 99%

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An Intensive and Comprehensive Overview of JAYA Algorithm, its Versions and Applications

Zitar

Al-Betar

Awadallah

et al. 2021

Arch Computat Methods Eng

124

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In this review paper, JAYA algorithm, which is a recent population-based algorithm is intensively overviewed. The JAYA algorithm combines the survival of the fittest principle from evolutionary algorithms as well as the global optimal solution attractions of Swarm Intelligence methods. Initially, the optimization model and convergence characteristics of JAYA algorithm are carefully analyzed. Thereafter, the proposed versions of JAYA algorithm have been surveyed such as modified, binary, hybridized, parallel, chaotic, multi-objective and others. The various applications tackled using relevant versions of JAYA algorithm are also discussed and summarized based on several problem domains. Furthermore, the open sources code of JAYA algorithm are identified to provide enrich resources for JAYA research communities. The critical analysis of JAYA algorithm reveals its advantages and limitations in dealing with optimization problems. Finally, the paper ends up with conclusion and possible future enhancements suggested to improve the performance of JAYA algorithm. The reader of this overview will determine the best domains and applications used by JAYA algorithm and can justify their JAYA-related contributions.

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“…The wrapper-based methods depend on the classification model and search algorithms. The hybrid feature selection [12,36] method uses both filter-based and wrapper-based method.…”

Section: Introductionmentioning

confidence: 99%

Optimal Feature Subset Selection Based on Combining Document Frequency and Term Frequency for Text Classification

Thirumoorthy¹,

Karuppaiah²

2020

cai

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Feature selection plays a vital role to reduce the high dimension of the feature space in the text document classification problem. The dimension reduction of feature space reduces the computation cost and improves the text classification system accuracy. Hence, the identification of a proper subset of the significant features of the text corpus is needed to classify the data in less computational time with higher accuracy. In this proposed research, a novel feature selection method which combines the document frequency and the term frequency (FS-DFTF) is used to measure the significance of a term. The optimal feature subset which is selected by our proposed work is evaluated using Naive Bayes and Support Vector Machine classifier with various popular benchmark text corpus datasets. The experimental outcome confirms that the proposed method has a better classification accuracy when compared with other feature selection techniques.

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Optimal feature subset selection using hybrid binary Jaya optimization algorithm for text classification

Cited by 28 publications

References 29 publications

A Chaotic Antlion Optimization Algorithm for Text Feature Selection

A Chaotic Antlion Optimization Algorithm for Text Feature Selection

An Intensive and Comprehensive Overview of JAYA Algorithm, its Versions and Applications

Optimal Feature Subset Selection Based on Combining Document Frequency and Term Frequency for Text Classification

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