RETRACTED ARTICLE: Feature selection using fish swarm optimization in big data

Manikandan, R.; Kalpana, A. M.

doi:10.1007/s10586-017-1182-z

Cited by 18 publications

(15 citation statements)

References 21 publications

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“…But this approach provided high accuracy only through the utilization of simple crossover and mutations, which makes the WOA achieving low performance individually. Manikandan and Kalpana [18] designed artificial fish swarm optimization (AFSO) based feature selection with Classification and regression tree (CART) to achieve 7.9% improved accuracy. Still this algorithm has limited convergence rate for larger datasets.…”

Section: Related Workmentioning

confidence: 99%

Population and Global Search Improved Squirrel Search Algorithm for Feature Selection in Big Data Classification

Ali¹,

Balakrishnan²

2021

IJIES

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The analytics techniques in Big Data are extensively employed as an alternative to generalized for data mining due to the huge volumes of large-scale high dimensional data. Feature selection techniques eradicate the redundant and inappropriate features to decrease the data dimensionality and increase the classifiers' efficiency. However, the traditional feature selection strategies are dearth of scalability to handle the unique characteristics of large-scale data and extract the valuable features within the restricted time. This article proposed a feature selection algorithm centered on the Population and Global Search Improved Squirrel Search Algorithm (PGS-ISSA) that tackles the problem of local optimum and reduce convergence rate in standard Squirrel Search Algorithm (SSA). The novelty of this proposed PGS-ISSA is the introduction of chaos theory to improve population initialization so that the search space is increased. Then the acceleration coefficients are used in the position update equations to improve the convergence rate in local search process while inertia weight is also applied to optimally balance the exploration and exploitation in SSA. PGS-ISSA employs the fitness function based on the minimum error rate for ensuring the selection of best features that improve the classification accuracy. The proposed PGS-ISSA based feature section algorithm is evaluated by using Support Vector Machine (SVM) classifier implemented in MATLAB tool to address the big data classification problem. The experiments performed on both small and large-scale datasets illustrated that the suggested PGS-ISSA enhances the classification accuracy by 1.7% to 5.4% better than other compared models through effective handling of the big data problems. The results obtained for the bigger Higgs dataset shows that the proposed PGS-ISSA achieved high performance than the standard SSA, existing ISSA models and other prominent optimizationbased feature selection algorithms with 64.72% accuracy, 67.3194% precision, 62.1528% recall, 62.3026% f-measure, 82.7226% specificity and consumed less time of 140.1366 seconds. PGS-ISSA also achieved comparatively better results for the other benchmark datasets with 0.3% to 6% improvement on statistical metrics and 10% to 25% reduction in execution time.

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Section: Related Workmentioning

confidence: 99%

Population and Global Search Improved Squirrel Search Algorithm for Feature Selection in Big Data Classification

Ali¹,

Balakrishnan²

2021

IJIES

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“…J. Vijaya in [65] proposed an algorithm for the telecom churn prediction that makes use of particle swarm optimization and proposes three different versions of PSO for churn prediction that is, PSO having feature selection as its pre-processing mechanism, PSO with simulated annealing and lastly PSO with a blend of both feature selection and simulated annealing. [66] proposed fish swarm optimization for feature selection in big data. To perform the experiments, Product review dataset that is obtained from Amazon along with synthetic data is used.…”

Section: Pedram Ghamisi Etal Inmentioning

confidence: 99%

Binary Multi-Verse Optimization (BMVO) Approaches for Feature Selection

Hans¹,

Kaur²

2020

IJIMAI

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Multi-Verse Optimization (MVO) is one of the newest meta-heuristic optimization algorithms which imitates the theory of Multi-Verse in Physics and resembles the interaction among the various universes. In problem domains like feature selection, the solutions are often constrained to the binary values viz. 0 and 1. With regard to this, in this paper, binary versions of MVO algorithm have been proposed with two prime aims: firstly, to remove redundant and irrelevant features from the dataset and secondly, to achieve better classification accuracy. The proposed binary versions use the concept of transformation functions for the mapping of a continuous version of the MVO algorithm to its binary versions. For carrying out the experiments, 21 diverse datasets have been used to compare the Binary MVO (BMVO) with some binary versions of existing metaheuristic algorithms. It has been observed that the proposed BMVO approaches have outperformed in terms of a number of features selected and the accuracy of the classification process.

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“…The CART has adapted data that has been numbered or with firm values to manage the feature ethics which were missed. The trimming of the complexity of cost has been used for generating the reversion trees [23].…”

Section: Classification and Regression Tree (Cart) Classifiermentioning

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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RETRACTED ARTICLE: Feature selection using fish swarm optimization in big data

Cited by 18 publications

References 21 publications

Population and Global Search Improved Squirrel Search Algorithm for Feature Selection in Big Data Classification

Population and Global Search Improved Squirrel Search Algorithm for Feature Selection in Big Data Classification

Binary Multi-Verse Optimization (BMVO) Approaches for Feature Selection

Feature Selection using Stochastic Diffusion Search Algorithm in Big Data Analysis

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