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

Devi, D. Renuka; Sasikala, S.

doi:10.1186/s40537-019-0267-3

Cited by 14 publications

(6 citation statements)

References 34 publications

(36 reference statements)

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“…However, the preprocessing operation is poor. The EIDMLP introduced in Reference 32 is good in classifying the features with higher Precision and F ‐measure. Since the computational complexity of this technique is lower, it is not preferred.…”

Section: Literature Reviewmentioning

confidence: 99%

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Improved meta‐heuristic algorithm for selecting optimal features: A big data classification model

Selvi¹,

Valarmathi²,

Devadas

2022

Concurrency and Computation

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Many fields function with large databases constitute a high number of features. Feature selection strategies seek to exclude the features that are distracting, repetitive, or unnecessary, as they can degrade the classification results. Existing approaches lack the scalability needed to handle the datasets with millions of instances and they do not obtain favorable results in a timely manner. This study uses a unique feature selection approach based on an upgraded optimization model and deep machine learning‐based data classification. “(a) Feature extraction, (b) optimal feature selection, and (c) classification” are the three stages of the proposed model. Initially, the extracted big‐datasets are efficiently handled by the parallel pool map‐reduce architecture. Several features from the input big‐data are extracted using feature extraction (FE) approaches such as the suggested Tri‐Kernel principal component analysis (TK‐PCA), linear discriminant analysis, and linear square regression. Furthermore, the data obtained characteristics may contain data that is irrelevant, out‐of‐date, or noisy. The computing cost rises due to the larger feature space. As a result, the best features are selected using a new optimization technique known as Levy Adapted SLnO (LA‐SLnO), which is a superior variant of the original SLnO algorithm. This selection of appropriate features improves the classification accuracy. For classification, Convolutional Neural Network is used in this work. Finally, a comparative evaluation is undergone to validate the efficiency of the proposed model.

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Section: Literature Reviewmentioning

confidence: 99%

“…In 2019, Devi et al 32 have developed a new framework referred as online feature selection–accelerated bat algorithm (OFS–ABA) in the feature space. With the map reduce framework, the authors have selected the nonsuperfluous and significant features in the OFS‐ABA method.…”

Section: Literature Reviewmentioning

confidence: 99%

Improved meta‐heuristic algorithm for selecting optimal features: A big data classification model

Selvi¹,

Valarmathi²,

Devadas

2022

Concurrency and Computation

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“…The BA in the domain of feature selection has shown outstanding results and proved its scalability in addressing a such complex and high-dimensional problems such as gene selection [ 7 , 11 , 22 , 55 , 66 ], disease detection [ 64 , 65 , 135 ], cancer classification [ 143 ], fault diagnosis [ 105 ], and online feature selection [ 247 ]. The BA applications in this domain are presented in Fig.…”

Section: Applications Of Bat-inspired Algorithmmentioning

confidence: 99%

Recent advances of bat-inspired algorithm, its versions and applications

Alyasseri

Alomari

Al-Betar

et al. 2022

Neural Comput & Applic

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Bat-inspired algorithm (BA) is a robust swarm intelligence algorithm that finds success in many problem domains. The ecosystem of bat animals inspires the main idea of BA. This review paper scanned and analysed the state-of-the-art researches investigated using BA from 2017 to 2021. BA has very impressive characteristics such as its easy-to-use, simple in concepts, flexible and adaptable, consistent, and sound and complete. It has strong operators that incorporate the natural selection principle through survival-of-the-fittest rule within the intensification step attracted by local-best solution. Initially, the growth of the recent solid works published in Scopus indexed articles is summarized in terms of the number of BA-based Journal articles published per year, citations, top authors, work with BA, top institutions, and top countries. After that, the different versions of BA are highlighted to be in line with the complex nature of optimization problems such as binary, modified, hybridized, and multiobjective BA. The successful applications of BA are reviewed and summarized, such as electrical and power system, wireless and network system, environment and materials engineering, classification and clustering, structural and mechanical engineering, feature selection, image and signal processing, robotics, medical and healthcare, scheduling domain, and many others. The critical analysis of the limitations and shortcomings of BA is also mentioned. The open-source codes of BA code are given to build a wealthy BA review. Finally, the BA review is concluded, and the possible future directions for upcoming developments are suggested such as utilizing BA to serve in dynamic, robust, multiobjective, large-scaled optimization as well as improve BA performance by utilizing structure population, tuning parameters, memetic strategy, and selection mechanisms. The reader of this review will determine the best domains and applications used by BA and can justify their BA-related contributions.

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“…In this fitness function, a combination of classification accuracy in the KNN classification algorithm and the sum of similarities between the selected features is used. The fit of the feature subset in the iteration denoted by ( ( )) is measured by Equation (5).…”

Section: Step 4: Calculate Fitness Valuesmentioning

confidence: 99%

“…Whenever the needed number of training examples cannot be provided, reducing features decreases the size of the needed training examples and hence increases the overall yield shape of the classification algorithm. In the previous years, two methods for dimensional reduction were presented: feature selection and feature extraction [4,5]. Feature selection (FS) seeks for a relevant subset of existing features, while features are designed for a new space of lower dimensionality in the feature extraction method.…”

Section: Introductionmentioning

confidence: 99%

A Novel Community Detection Based Genetic Algorithm for Feature Selection

Rostami

Berahmand

Forouzandeh

2020

Preprint

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The selection of features is an essential data preprocessing stage in data mining. The core principle of feature selection seems to be to pick a subset of possible features by excluding features with almost no predictive information as well as highly associated redundant features. In the past several years, a variety of meta-heuristic methods were introduced to eliminate redundant and irrelevant features as much as possible from high-dimensional datasets. Among the main disadvantages of present meta-heuristic based approaches is that they are often neglecting the correlation between a set of selected features. In this article, for the purpose of feature selection, the authors propose a genetic algorithm based on community detection, which functions in three steps. The feature similarities are calculated in the first step. The features are classified by community detection algorithms into clusters throughout the second step. In the third step, features are picked by a genetic algorithm with a new community-based repair operation. Nine benchmark classification problems were analyzed in terms of the performance of the presented approach. Also, the authors have compared the efficiency of the proposed approach with the findings from four available algorithms for feature selection. The findings indicate that the new approach continuously yields improved classification accuracy.

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

Cited by 14 publications

References 34 publications

Improved meta‐heuristic algorithm for selecting optimal features: A big data classification model

Improved meta‐heuristic algorithm for selecting optimal features: A big data classification model

Recent advances of bat-inspired algorithm, its versions and applications

A Novel Community Detection Based Genetic Algorithm for Feature Selection

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