Feature selection based on an improved cat swarm optimization algorithm for big data classification

Lin, Kuan‐Cheng; Zhang, Kai-Yuan; Huang, Yi‐Hung; Hung, Jason C.; Yen, Neil Y.

doi:10.1007/s11227-016-1631-0

Cited by 108 publications

(46 citation statements)

References 11 publications

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“…Additionally, Mohapatra et al used the idea of using mutation operation before distributing the cats into seeking or tracing modes [30]. [32]. It is worth mentioning that there are four versions of CSO referenced in [19,23,24,32], all having the same name (ICSO).…”

Section: Modified Cat Swarm Optimization (Mcso): Lin Et Al Combined mentioning

confidence: 99%

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Cat Swarm Optimization Algorithm - A Survey and Performance Evaluation

Ahmed¹,

Rashid²,

Saeed³

2020

Preprint

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This paper presents an in-depth survey and performance evaluation of the Cat Swarm Optimization (CSO) Algorithm. CSO is a robust and powerful metaheuristic swarm-based optimization approach that has received very positive feedback since its emergence. It has been tackling many optimization problems and many variants of it have been introduced. However, the literature lacks a detailed survey or a performance evaluation in this regard. Therefore, this paper is an attempt to review all these works, including its developments and applications, and group them accordingly. In addition, CSO is tested on 23 classical benchmark functions and 10 modern benchmark functions (CEC 2019). The results are then compared against three novel and powerful optimization algorithms, namely Dragonfly algorithm (DA), Butterfly optimization algorithm (BOA) and Fitness Dependent Optimizer (FDO). These algorithms are then ranked according to Friedman test and the results show that CSO ranks first on the whole. Finally, statistical approaches are employed to further confirm the outperformance of CSO algorithm.

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Section: Modified Cat Swarm Optimization (Mcso): Lin Et Al Combined mentioning

confidence: 99%

“…[32]. It is worth mentioning that there are four versions of CSO referenced in [19,23,24,32], all having the same name (ICSO). However, their structures are different.…”

Section: Modified Cat Swarm Optimization (Mcso): Lin Et Al Combined mentioning

confidence: 99%

Cat Swarm Optimization Algorithm - A Survey and Performance Evaluation

Ahmed¹,

Rashid²,

Saeed³

2020

Preprint

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“…Lin et al, [14] proposed an improved ICSO algorithm. The work was further applied to the ICSO algorithm for choosing all the select features in their experiment of text classification in the Big Data.…”

Section: Related Workmentioning

confidence: 99%

Feature Selection using Stochastic Diffusion Search Algorithm in Big Data Analysis

2020

IJRTE

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Abstract: Big Data analysis has been viewed as the processing or mining of massive amounts of data used to retrieve information which is useful from large datasets. Among all the methods employed to deal with the analysis of Big Data, the selection of a feature is found extremely effective. A common approach which includes search making use of feature-based subsets which is relevant to the topic, tends to represent the dataset with its actual description. However, a search that makes use of such a subset is a combinatorial problem which is time-consuming. All commonly used meta-heuristic algorithms to facilitate feature choice. The Stochastic Diffusion Search (SDS) based algorithm has been a multi-agent global search algorithm based on agent interaction is simple to overcome combinatorial problems. The SDS will choose the feature subset for the task of classification. The Classification and Regression Tree (CART), the Naïve Bayes (NB), the Support Vector Machine (SVM) and the K-Nearest Neighbour (KNN) have beenused to improve the performance. Results proved that the proposed method was able to achieve a better performance than existing techniques.

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“…Filter and Wrapper methods of FS have been applied by many researchers for classification problems to select candidate subset(reduced feature set) that increase the performance of classifiers [5 ] [ 6]. In addition to these two methods, embedded FS approach also have been applied for the classification problems as part of modeling process.…”

Section: Related Studymentioning

confidence: 99%

A Novel Clustering Based Candidate Feature Selection Framework Using Correlation Coefficient for Improving Classification Performance

Potharaju¹,

Sreedevi²

2017

JESTR

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Feature Selection (FS) is an imperative issue in data mining and machine learning. It is an inevitable task to shorter the number of features presented in the initial data set for better classification result, minimized computation time, and reduced memory consumption. In this article, a novel framework using Correlation Coefficient (CCE) and Symmetrical Uncertainty (SU) for selecting the subset of feature is proposed. The selected features are congregated into finite number of clusters by grading their CCE and comparing the SU values. In each cluster, a feature with maximum SU value is retained while remaining features in the same cluster are ignored. The proposed framework was examined with Ten(10) real time benchmark data sets. Experimental outcomes show that the proposed method is outruns than majority of conventional feature selection methods(Information Gain, Chi-Square, Gain Ratio, ReliefF) in accuracy. This method is tested using Tree Based, Rule Based, Lazy, and Naive Bayes learners.

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Feature selection based on an improved cat swarm optimization algorithm for big data classification

Cited by 108 publications

References 11 publications

Cat Swarm Optimization Algorithm - A Survey and Performance Evaluation

Cat Swarm Optimization Algorithm - A Survey and Performance Evaluation

Feature Selection using Stochastic Diffusion Search Algorithm in Big Data Analysis

A Novel Clustering Based Candidate Feature Selection Framework Using Correlation Coefficient for Improving Classification Performance

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