Online feature selection system for big data classification based on multi-objective automated negotiation

2021 ◽  
Vol 110 ◽  
pp. 107629
Author(s):  
Fatma BenSaid ◽  
Adel M. Alimi
Keyword(s):  
Feature Selection ◽  
Big Data ◽  
Data Classification ◽  
Automated Negotiation ◽  
Selection System ◽  
Multi Objective ◽  
Online Feature Selection ◽  
Big Data Classification

scholarly journals An Intelligent Metaheuristic Binary Pigeon Optimization-Based Feature Selection and Big Data Classification in a MapReduce Environment

Mathematics ◽  
2021 ◽  
Vol 9 (20) ◽  
pp. 2627
Author(s):  
Felwa Abukhodair ◽  
Wafaa Alsaggaf ◽  
Amani Tariq Jamal ◽  
Sayed Abdel-Khalek ◽  
Romany F. Mansour
Keyword(s):  
Feature Selection ◽  
Big Data ◽  
Short Term Memory ◽  
Programming Model ◽  
Data Classification ◽  
Massive Data ◽  
Effective Performance ◽  
Different Dimensions ◽  
Long Short Term Memory ◽  
Big Data Classification

Big Data are highly effective for systematically extracting and analyzing massive data. It can be useful to manage data proficiently over the conventional data handling approaches. Recently, several schemes have been developed for handling big datasets with several features. At the same time, feature selection (FS) methodologies intend to eliminate repetitive, noisy, and unwanted features that degrade the classifier results. Since conventional methods have failed to attain scalability under massive data, the design of new Big Data classification models is essential. In this aspect, this study focuses on the design of metaheuristic optimization based on big data classification in a MapReduce (MOBDC-MR) environment. The MOBDC-MR technique aims to choose optimal features and effectively classify big data. In addition, the MOBDC-MR technique involves the design of a binary pigeon optimization algorithm (BPOA)-based FS technique to reduce the complexity and increase the accuracy. Beetle antenna search (BAS) with long short-term memory (LSTM) model is employed for big data classification. The presented MOBDC-MR technique has been realized on Hadoop with the MapReduce programming model. The effective performance of the MOBDC-MR technique was validated using a benchmark dataset and the results were investigated under several measures. The MOBDC-MR technique demonstrated promising performance over the other existing techniques under different dimensions.

scholarly journals MapReduce-based big data classification model using feature subset selection and hyperparameter tuned deep belief network

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Surendran Rajendran ◽  
Osamah Ibrahim Khalaf ◽  
Youseef Alotaibi ◽  
Saleh Alghamdi
Keyword(s):  
Feature Selection ◽  
Big Data ◽  
Selection Process ◽  
Data Classification ◽  
Deep Belief Network ◽  
Feature Subset Selection ◽  
Classification Model ◽  
Feature Subset ◽  
Belief Network ◽  
Big Data Classification

AbstractIn recent times, big data classification has become a hot research topic in various domains, such as healthcare, e-commerce, finance, etc. The inclusion of the feature selection process helps to improve the big data classification process and can be done by the use of metaheuristic optimization algorithms. This study focuses on the design of a big data classification model using chaotic pigeon inspired optimization (CPIO)-based feature selection with an optimal deep belief network (DBN) model. The proposed model is executed in the Hadoop MapReduce environment to manage big data. Initially, the CPIO algorithm is applied to select a useful subset of features. In addition, the Harris hawks optimization (HHO)-based DBN model is derived as a classifier to allocate appropriate class labels. The design of the HHO algorithm to tune the hyperparameters of the DBN model assists in boosting the classification performance. To examine the superiority of the presented technique, a series of simulations were performed, and the results were inspected under various dimensions. The resultant values highlighted the supremacy of the presented technique over the recent techniques.

Rider Chaotic Biography Optimization-driven Deep Stacked Auto-encoder for Big Data Classification Using Spark Architecture

2021 ◽  
Vol 18 (3) ◽  
pp. 42-62
Author(s):  
Anilkumar V Brahmane ◽  
Chaitanya B Krishna
Keyword(s):  
Feature Selection ◽  
Big Data ◽  
Optimization Algorithm ◽  
Imbalanced Data ◽  
Data Classification ◽  
Software Tools ◽  
Novel Technique ◽  
Big Data Classification ◽  
Spark Framework ◽  
Day By Day

The novelty in big data is rising day-by-day in such a way that the existing software tools face difficulty in supervision of big data. Furthermore, the rate of the imbalanced data in the huge datasets is a key constraint to the research industry. Thus, this paper proposes a novel technique for handling the big data using Spark framework. The proposed technique undergoes two steps for classifying the big data, which involves feature selection and classification, which is performed in the initial nodes of Spark architecture. The proposed optimization algorithm is named rider chaotic biography optimization (RCBO) algorithm, which is the integration of the rider optimization algorithm (ROA) and the standard chaotic biogeography-based optimisation (CBBO). The proposed RCBO deep-stacked auto-encoder using Spark framework effectively handles the big data for attaining effective big data classification. Here, the proposed RCBO is employed for selecting suitable features from the massive dataset.

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