A review of traditional and swarm search based feature selection algorithms for handling data stream classification

Sasikala, S.; Devi, D. Renuka

doi:10.1109/ssps.2017.8071650

Cited by 6 publications

(3 citation statements)

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“…The precision, recall, F-measure, and accuracy are the metrics used to assess the performance of this research work. MR-OFS-ABA method has shown enhanced performance than the existing feature selection methods namely PSO, APSO and ASAMO (Accelerated Simulated Annealing and Mutation Operator) [37,40]. The result of the EIDMLP classifier is compared with other existing classifiers such as Naïve Bayes (NB), Hoeffding Tree (HT) and FMCCSC (Fuzzy Minimal Consistent Class Subset Coverage (FMCCSC)-KNN (K Nearest Neighbour).…”

Section: Resultsmentioning

confidence: 99%

Online Feature Selection (OFS) with Accelerated Bat Algorithm (ABA) and Ensemble Incremental Deep Multiple Layer Perceptron (EIDMLP) for big data streams

Devi

Sasikala

2019

J Big Data

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Feature selection is mainly used to lessen the dispensation load of data mining models. To condense the time for processing voluminous data, parallel processing is carried out with MapReduce (MR) technique. However with the existing algorithms, the performance of the classifiers needs substantial improvement. MR method, which is recommended in this research work, will perform feature selection in parallel which progresses the performance. To enhance the efficacy of the classifier, this research work proposes an innovative Online Feature Selection (OFS)-Accelerated Bat Algorithm (ABA) and a framework for applications that streams the features in advance with indefinite knowledge of the feature space. The concrete OFS-ABA method is suggested to select significant and non-superfluous feature with MapReduce (MR) framework. Finally, Ensemble Incremental Deep Multiple Layer Perceptron (EIDMLP) classifier is applied to classify the dataset samples. The outputs of homogeneous IDMLP classifiers were combined using the EIDMPL classifier. The projected feature selection method along with the classifier is evaluated expansively on three datasets of high dimensionality. In this research work, MR-OFS-ABA method has shown enhanced performance than the existing feature selection methods namely PSO, APSO and ASAMO (Accelerated Simulated Annealing and Mutation Operator). The result of the EIDMLP classifier is compared with other existing classifiers such as Naïve Bayes (NB), Hoeffding tree (HT), and Fuzzy Minimal Consistent Class Subset Coverage (FMCCSC)-KNN (K Nearest Neighbour). The methodology is applied to three datasets and results were compared with four classifiers and three state-of-the-art feature selection algorithms. The outcome of this research work has shown enhanced performance in accuracy and less processing time.

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Section: Resultsmentioning

confidence: 99%

Online Feature Selection (OFS) with Accelerated Bat Algorithm (ABA) and Ensemble Incremental Deep Multiple Layer Perceptron (EIDMLP) for big data streams

Devi

Sasikala

2019

J Big Data

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“…Even though, comprehensive computing method may be employed for optimal feature subset selection, but is not the same in handling the high data streams which are gathered at a rapid rate. Amongst the various techniques, evolutionary methods have been successfully used for FS [19]. The excessive addition of the feature space leads to time complexity.…”

Section: Introductionmentioning

confidence: 99%

“…The aim of the proposed ASAMO algorithm is to have a combination of classification as well as FS algorithm as one. The review of the feature selection methods for handling data stream is also discussed in the recent work [18][19][20][21].…”

Section: Introductionmentioning

confidence: 99%

Accelerated Simulated Annealing and Mutation Operator Feature Selection method for Big Data

Devi¹,

Sasikala²

2019

IJRTE

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The optimal feature subset selection over very high dimensional data is a vital issue. Even though the optimal features are selected, the classification of those selected features becomes a key complicated task. In order to handle these problems, a novel, Accelerated Simulated Annealing and Mutation Operator (ASAMO) feature selection algorithm is suggested in this work. For solving the classification problem, the Fuzzy Minimal Consistent Class Subset Coverage (FMCCSC) problem is introduced. In FMCCSC, consistent subset is combined with the K-Nearest Neighbour (KNN) classifier known as FMCCSC-KNN classifier. The two data sets Dorothea and Madelon from UCI machine repository are experimented for optimal feature selection and classification. The experimental results substantiate the efficiency of proposed ASAMO with FMCCSC-KNN classifier compared to Particle Swarm Optimization (PSO) and Accelerated PSO feature selection algorithms.

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Feature Selection and Classification of Big Data Using MapReduce Framework

Devi¹,

Sasikala²

2019

Advances in Intelligent Systems and Computing

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A review of traditional and swarm search based feature selection algorithms for handling data stream classification

Cited by 6 publications

References 30 publications

Online Feature Selection (OFS) with Accelerated Bat Algorithm (ABA) and Ensemble Incremental Deep Multiple Layer Perceptron (EIDMLP) for big data streams

Online Feature Selection (OFS) with Accelerated Bat Algorithm (ABA) and Ensemble Incremental Deep Multiple Layer Perceptron (EIDMLP) for big data streams

Accelerated Simulated Annealing and Mutation Operator Feature Selection method for Big Data

Feature Selection and Classification of Big Data Using MapReduce Framework

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