Adaptive Exponential Bat algorithm and deep learning for big data classification

Mujeeb, Sana; Sam, R. Praveen; Madhavi, K.

doi:10.1007/s12046-020-01521-z

Cited by 11 publications

(4 citation statements)

References 21 publications

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“…The reported results of both methods LC-KNN, and RC-KNN showed better performance when tested on Big datasets. It's also worth highlighting other Big Data classification-related studies, like as [77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,105,108,93,109,110,111,112,113,114,115,114,116,117,118,119,120,121,122,123,124,…”

Section: Literature Reviewmentioning

confidence: 99%

Magnetic Force Classifier: A Novel Method for Big Data Classification

et al. 2022

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There are a plethora of invented classifiers in Machine learning literature, however, there is no optimal classifier in terms of accuracy and time taken to build the trained model, especially with the tremendous development and growth of Big data. Hence, there is still room for improvement. In this paper, we propose a new classification method that is based on the well-known magnetic force. Based on the number of points belonging to a specific class/magnet, the proposed magnetic force (MF) classifier calculates the magnetic force at each discrete point in the feature space. Unknown examples are classified using the magnetic forces recorded in the trained model by various magnets/classes. When compared to existing classifiers, the proposed MF classifier achieves comparable classification accuracy, according to the experimental results utilizing 28 different datasets. More importantly, we found that the proposed MF classifier is significantly faster than all other classifiers tested, particularly when applied to Big datasets and hence could be a viable option for structured Big data classification with some optimization.

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Section: Literature Reviewmentioning

confidence: 99%

Magnetic Force Classifier: A Novel Method for Big Data Classification

et al. 2022

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“…In the classification, clustering, and forecasting domain, the BA is successfully applicable in several problems such as prediction and classification [ 18 , 20 , 74 , 123 , 125 , 183 , 185 , 195 , 205 , 212 , 245 , 285 , 294 , 307 ], data clustering [ 17 , 27 , 46 , 98 , 223 , 234 , 238 , 295 , 301 , 345 , 346 ], data forecasting [ 39 , 168 , 243 , 335 ], and train the feed forward neural network [ 75 , 171 , 213 ]. These applications are shown in Fig 15 .…”

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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“…The model obtains the effective classification, but it is not applicable for the noisy data. Mujeeb et al [58] developed deep learning for big data classification. The Adaptive Exponential Bat algorithm is devised for training the classifier.…”

Section: Literature Reviewmentioning

confidence: 99%

Analysis of Bayesian optimization algorithms for big data classification based on Map Reduce framework

Banchhor

Srinivasu

2021

J Big Data

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The process of big data handling refers to the efficient management of storage and processing of a very large volume of data. The data in a structured and unstructured format require a specific approach for overall handling. The classifiers analyzed in this paper are correlative naïve Bayes classifier (CNB), Cuckoo Grey wolf CNB (CGCNB), Fuzzy CNB (FCNB), and Holoentropy CNB (HCNB). These classifiers are based on the Bayesian principle and work accordingly. The CNB is developed by extending the standard naïve Bayes classifier with applied correlation among the attributes to become a dependent hypothesis. The cuckoo search and grey wolf optimization algorithms are integrated with the CNB classifier, and significant performance improvement is achieved. The resulting classifier is called a cuckoo grey wolf correlative naïve Bayes classifier (CGCNB). Also, the performance of the FCNB and HCNB classifiers are analyzed with CNB and CGCNB by considering accuracy, sensitivity, specificity, memory, and execution time.

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Adaptive Exponential Bat algorithm and deep learning for big data classification

Cited by 11 publications

References 21 publications

Magnetic Force Classifier: A Novel Method for Big Data Classification

Magnetic Force Classifier: A Novel Method for Big Data Classification

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

Analysis of Bayesian optimization algorithms for big data classification based on Map Reduce framework

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