A hybrid algorithm using ant and bee colony optimization for feature selection and classification (AC-ABC Hybrid)

Shunmugapriya, P.; Kanmani, S.

doi:10.1016/j.swevo.2017.04.002

Cited by 190 publications

(74 citation statements)

References 26 publications

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“…Fluctuation may occur when huge numbers of fireflies attract to light emission source and the searching process becomes time-consuming. To overcome these issues, neighborhood attraction FA (NaFA) is introduced, which shows that fireflies are just attracted to only some brighter points, which are outlined by previous neighbor [62].…”

Section: Firefly Algorithm (Fa)mentioning

confidence: 99%

Literature Review on Big Data Analytics Methods

Vanani¹,

Majidian²

2020

Social Media and Machine Learning

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Companies and industries are faced with a huge amount of raw data, which have information and knowledge in their hidden layer. Also, the format, size, variety, and velocity of generated data bring complexity for industries to apply them in an efficient and effective way. So, complexity in data analysis and interpretation incline organizations to deploy advanced tools and techniques to overcome the difficulties of managing raw data. Big data analytics is the advanced method that has the capability for managing data. It deploys machine learning techniques and deep learning methods to benefit from gathered data. In this research, the methods of both ML and DL have been discussed, and an ML/DL deployment model for IOT data has been proposed.

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Section: Firefly Algorithm (Fa)mentioning

confidence: 99%

Literature Review on Big Data Analytics Methods

Vanani¹,

Majidian²

2020

Social Media and Machine Learning

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“…Ghosh et al [32] proposed an adaptive Differential Evolution (DE) algorithm for feature subset selection in hyperspectral image data, in which the parameters of DE are adjusted by the algorithm itself depending on the type of problem at hand. Shunmugapriya and Kanmani [33] proposed a hybrid algorithm which combines ACO and Artificial Bee Colony (ABC) algorithms for feature subset selection in classification, where each ant exploit by the bees to find the best ant of colony and each bee adapts their food source by the ants. Zorarpacı and Özel [34] proposed a hybrid algorithm that combines ABC and DE for feature subset selection in classification.…”

Section: Literature Reviewmentioning

confidence: 99%

“…In the comparison, 17 algorithms are implemented and tested on Matlab 2015b, all based on the well-known k-NN classifier with k = 5. To show the performance of the proposed algorithm, we compare it with the standard GA, the original CSO algorithm [25], the original PSO, DE [32], ABC-DE [34], ACO-FS [31], ACO-ABC [33], GSA [56], BQIGSA [57], four variants of PSO proposed by Xue's for bi-objective feature subset selection [36] (Xue1-PSO, Xue2-PSO, Xue3-PSO, and Xue4-PSO), and three two-stage algorithms include 2S-GA [40], 2S-HGA [41], and 2S-PSO [39]. Based on Xue et al [36], the major difference between Xue's algorithms is the number of features selected in the initial swarm, while Xue1-PSO uses the normal initialization method where approximately half of the features are chosen in each particles, Xue2-PSO applies a little initialization method in which only about 10% features are chosen in each particles, Xue3-PSO applies heavy initialization method in which more than half (about 2/3) of the features are chosen in each particles, and Xue4-PSO applies a combined initialization in which a major (about 2/3) of the particles are initialized with the little initialization method, while the remaining particles of swarm are initialized with the heavy initialization method.…”

Section: Dataset Properties and Experimental Settingsmentioning

confidence: 99%

A Novel Three-Stage Filter-Wrapper Framework for miRNA Subset Selection in Cancer Classification

2018

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Micro-Ribonucleic Acids (miRNAs) are small non-coding Ribonucleic Acid (RNA) molecules that play an important role in the cancer growth. There are a lot of miRNAs in the human body and not all of them are responsible for cancer growth. Therefore, there is a need to propose the novel miRNA subset selection algorithms to remove irrelevant and redundant miRNAs and find miRNAs responsible for cancer development. This paper tries to propose a novel three-stage miRNAs subset selection framework for increasing the cancer classification accuracy. In the first stage, multiple filter algorithms are used for ranking the miRNAs according to their relevance with the class label, and then generating a miRNA pool obtained based on the top-ranked miRNAs of each filter algorithm. In the second stage, we first rank the miRNAs of the miRNA pool by multiple filter algorithms and then this ranking is used to weight the probability of selecting each miRNA. In the third stage, Competitive Swarm Optimization (CSO) tries to find an optimal subset from the weighed miRNAs of the miRNA pool, which give us the most information about the cancer patients. It should be noted that the balance between exploration and exploitation in the proposed algorithm is accomplished by a zero-order Fuzzy Inference System (FIS). Experiments on several miRNA cancer datasets indicate that the proposed three-stage framework has a great performance in terms of both the low error rate of the cancer classification and minimizing the number of miRNAs.

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“…Feature selection can be categorized into filter and wrapper approaches. Filter approach makes use of the dependency, mutual information, distance, and information theory in feature selection [14]. Unlike filter, wrapper approach employs a classifier as the learning algorithm to optimize the classification performance by selecting the relevant features.…”

Section: Introductionmentioning

confidence: 99%

A New Competitive Binary Grey Wolf Optimizer to Solve the Feature Selection Problem in EMG Signals Classification

et al. 2018

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Features extracted from the electromyography (EMG) signal normally consist of irrelevant and redundant features. Conventionally, feature selection is an effective way to evaluate the most informative features, which contributes to performance enhancement and feature reduction. Therefore, this article proposes a new competitive binary grey wolf optimizer (CBGWO) to solve the feature selection problem in EMG signals classification. Initially, short-time Fourier transform (STFT) transforms the EMG signal into time-frequency representation. Ten time-frequency features are extracted from the STFT coefficient. Then, the proposed method is used to evaluate the optimal feature subset from the original feature set. To evaluate the effectiveness of proposed method, CBGWO is compared with binary grey wolf optimization (BGWO1 and BGWO2), binary particle swarm optimization (BPSO), and genetic algorithm (GA). The experimental results show the superiority of CBGWO not only in classification performance, but also feature reduction. In addition, CBGWO has a very low computational cost, which is more suitable for real world application.

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A hybrid algorithm using ant and bee colony optimization for feature selection and classification (AC-ABC Hybrid)

Cited by 190 publications

References 26 publications

Literature Review on Big Data Analytics Methods

Literature Review on Big Data Analytics Methods

A Novel Three-Stage Filter-Wrapper Framework for miRNA Subset Selection in Cancer Classification

A New Competitive Binary Grey Wolf Optimizer to Solve the Feature Selection Problem in EMG Signals Classification

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