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big data classification of learning behaviour based on data reduction and ensemble learning

INTERNATIONAL JOURNAL OF CONTINUING ENGINEERING EDUCATION AND LIFE-LONG LEARNING 2023年第4-5期33卷 496-510页

作者： Wang, Taotao Wu, Xiaoxuan Jiangxi Univ Technol Dept Informat Engn Coll Ganzhou 330098 Jiangxi Peoples R China Guangxi Vocat Coll Water Resources & Elect Power Dept Gen Educ Nanning 530023 Peoples R China

In order to overcome the problems of low classification accuracy, long time, and high missing ratio of traditional methods, a big data classification method of learning behaviour based on data reduction and ensemble learning was proposed. By cleaning and transforming the big data of learning behaviour and discretising the attributes of big data of learning behaviour, the data reduction algorithm is used to simplify the attributes of big data of learning behaviour. The ensemble learning method is used to linearly combine several weak classifiers, and the ensemble classifier is trained according to Choquet integral. The trained classifier is used to classify the big data of learning behaviour after simplified processing. The experimental results show that when the amount of big data on learning behaviour reaches 5,000 GB, the average classification accuracy of the proposed method is 92%, the classification time is 29 s, and the failure rate of classification is 0.32%.

关键词： data reduction ensemble learning rough set theory big data of learning behaviour big data classification

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Optimized Decision tree rules using divergence based grey wolf optimization for big data classification in health care

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EVOLUTIONARY INTELLIGENCE 2022年第2期15卷 971-987页

作者： Game, Pravin S. Vaze, Vinod Emmanuel, M. Shri JJT Univ Jhunjhunu Rajasthan India Pune Inst Comp Technol Pune Maharashtra India

Most of the organizations are mainly focusing on large datasets for automatic mining of necessary information from big medical data. The major issue of the big medical data is about its complex data sets and volume, which is gradually increasing. This paper intends to propose a big data classification model (heart disease) in health care, which includes certain phases or steps. The steps are as follows: (1) Map-reduce framework (2) support vector machine (SVM) (3) optimized decision tree classifier (DT). Initially, the big data is supplied as the input to the MapReduce Framework, where it reduces the data content through some major operations. This framework uses the principle component analysis to reduce the dimensions of data. The reduced data is subjected to SVM, where it outputs the classes. The output data from SVM is processed with a new contribution called 'data transformation' that paves way for optimal rule generation in decision tree classifier. The advanced optimization concept is involved in this process to optimize the weight and integer in data transformation. This paper introduces a new algorithm namely divergence based grey wolf optimization (DGWO). Finally, the transformed data is subjected to DT, where the classification takes place. The proposed DGWO model is compared over other conventional methods like firefly algorithm, artificial bee colony algorithm, particle swarm optimization algorithm, genetic algorithm and grey wolf optimizer algorithms.

关键词： big data big data classification Medical data Support vector machine Grey wolf optimizer

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Grey Wolf Shuffled Shepherd Optimization Algorithm-Based Hybrid Deep Learning Classifier for big data classification

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INTERNATIONAL JOURNAL OF SWARM INTELLIGENCE RESEARCH 2022年第1期13卷 1-20页

作者： Banchhor, Chitrakant Srinivasu, N. MIT Coll Engn Sch Comp Engn & Technol Pune Maharashtra India Koneru Lakshmaiah Educ Fdn Dept Comp Sci & Engn Vaddeswaram Andhra Pradesh India

In recent days, big data is a vital role in information knowledge analysis, predicting, and manipulating process. Moreover, big data is well-known for organized extraction and analysis of large or difficult databases. Furthermore, it is widely useful in data management as compared with the conventional data processing approach. The development in big data is highly increasing gradually, such that traditional software tools faced various issues during big data handling. However, data imbalance in huge databases is a main limitation in the research area. In this paper, the Grey wolf Shuffled Shepherd Optimization Algorithm (GWSSOA)-based Deep Recurrent Neural Network (DRNN) algorithm is devised to classify the big data. In this technique, for classifying the big data a hybrid classifier, termed as Holoentropy driven Correlative Naive Bayes classifier (HCNB) and DRNN classifier is introduced. In addition, the developed hybrid classification model utilizes the Map-Reduce structure to solve big data issues. Here, the training process of the DRNN classifier is employed using GWSSOA. However, the developed GWSSOA is devised by integrating Shuffled Shepherd Optimization Algorithm (SSOA) and Grey Wolf Optimizer (GWO) algorithms. The developed GWSSOA-based DRNN model outperforms other big data classification techniques with regards to accuracy, specificity, and sensitivity of 0.966, 0.964, 0.870, and 209837ms.

关键词： big data classification Deep Recurrent Neural Network Grey Wolf Optimizer MapReduce Framework Shuffled Shepherd Optimization Algorithm

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Ensemble Learning Approaches for big data classification Tasks 10

Ensemble Learning Approaches for Big Data Classification Tas...

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10th IEEE Uttar Pradesh Section International Conference on Electrical, Electronics and Computer Engineering, UPCON 2023

作者： Aswini, Kilaru Reddy, Uma Nagpal, Amandeep Rana, Ajay Praveen Abood, Baydaa Sh. Z. Institute of Aeronautical Engineering Hyderabad Dundigal India New Horizon College of Engineering Department of Artificial Intelligence and Machine Learning Bangalore India Lovely Professional University Phagwara India Director General Amity University Greater Noida India Lloyd Institute of Engineering & Technology Greater Noida India College of Engineering Technology National University of Science and Technology Dhi Qar Iraq

ISBN: (纸本)9798350382471

Ensemble learning has emerged as a potent method for improving prediction accuracy in big data classification tasks. This paper presents a comprehensive study of ensemble learning techniques, specifically focusing on their applicability and performance in handling vast and complex datasets. A detailed exploration of various ensemble methodologies such as bagging, boosting, and stacking is conducted, with a particular emphasis on their adaptability to big data challenges. The study further delves into novel hybrid ensemble models that synergize multiple learning algorithms to capitalize on their individual strengths. A quantitative analysis is performed on several benchmark datasets to evaluate the performance of these ensemble strategies against standalone classifiers. The results indicate a significant enhancement in classification accuracy, robustness, and error reduction, underlining the efficacy of ensemble approaches in the big data domain. The paper also introduces a framework for dynamic ensemble selection, which intelligently chooses a subset of models tailored to the specific characteristics of the dataset in question. This adaptability showcases the potential of ensemble methods in evolving data landscapes, making them invaluable tools for big data practitioners. The implications of these findings suggest a paradigm shift in predictive modeling, steering future research towards more adaptive, scalable, and accurate ensemble systems. © 2023 IEEE.

关键词： Bagging big data classification Boosting Ensemble Learning Hybrid Models

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big data classification of remote sensing image based on cloud computing and convolutional neural network

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SOFT COMPUTING 2022年

作者： Wu, Xiaobo Lingnan Normal Univ Sch Geog Sci Zhanjiang 524048 Guangdong Peoples R China

In recent years, with the continuous development of cloud computing and big data technology, information technology is penetrating all corners of enterprise development at a speed that ordinary people cannot imagine. Today's remote sensing image classification plays an important role in many applications. This article is based on cloud computing and convolutional neural network to study the remote sensing image big data classification. The article aims to fully focus on cloud computing technology and convolutional neural network technology to carry out specific application research, on this basis, to obtain a more convenient, accurate and efficient remote sensing image classification method, and to prove the feasibility of this method. This article takes cloud computing technology and convolutional neural network technology as the technical points. First, it introduces cloud computing technology in more detail from the basic concepts, characteristics and classification of cloud computing and then compares traditional methods from the selection of feature extraction methods and classifiers. The remote sensing image classification method is described, and finally, the convolutional neural network model that needs to be applied in this research is explained. In the experiment, the features extracted by multiple pre-training networks are fused. Through the study of the three feature fusion methods, it is found that the classification accuracy rate has been further improved on the three data sets. The experimental results in this paper show that the accuracy of the traditional method is below 80%, while the feature extraction of the pre-trained network has an accuracy of more than 90% on the UCMERCE data;it combines the three pre-trained networks of ResNet-50, ResNet-101 and ResNet-152, an increase of more than 2.5%.

关键词： Remote Sensing Image Cloud Computing Convolutional Neural Network big data classification

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Energy-and trust-aware secure routing algorithm for big data classification using MapReduce framework in IoT networks

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International Journal of Modeling, Simulation, and Scientific Computing 2021年第2期12卷 80-103页

作者： S.Md.Mujeeb R.Praveen Sam K.Madhavi Department of CSE Jawaharlal Nehru Technological University Anantapur(JNTUA)Ananthapuramu-515002 Andhra PradeshIndia Department of Computer Science and Engineering G.Pulla Reddy Engineering College(Autonomous)Nandyal Road Kurnool 518007Andhra PradeshIndia

Due to the open nature of wireless data transmission,routing and data security pose an important research challenge in the Internet of Things(IoT)-enabled ***,the characteristic features,like constrained resources,heterogeneity,uncontrolled environment,and scalability requirement,make the security issues even more ***,an effective and secure routing protocol named modified Energy Harvesting Trust-aware Routing Algorithm(mod-EHTARA)is proposed to increase the energy efficiency and the lifespan of the *** proposed mod-EHTARA is designed by adopting the Link Lifetime(LLT)model with the traditional *** optimal secure routing path is effectively selected by the proposed mod-EHTARA using the cost metric,which considers the factors like delay,LLT,energy,and *** big data classification process is carried out at the Base Station(BS)using the MapReduce ***,the big data classification is progressed using a stacked autoencoder,which is trained by the Adaptive E-Bat *** Adaptive E-Bat algorithm is developed by integrating the adaptive concept with the Bat Algorithm(BA)and Exponential Weighted Moving Average(EWMA).The proposed mod-EHTARA showed better performance by obtaining a maximal energy of 0.9855.

关键词： Stacked autoencoder big data classification Internet of Things secure routing energy harvesting

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Ensemble classifier based big data classification with hybrid optimal feature selection

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ADVANCES IN ENGINEERING SOFTWARE 2022年 173卷

作者： Pamila, J. C. Miraclin Joyce Selvi, R. Senthamil Santhi, P. Nithya, T. M. Govt Coll Technol Dept Comp Sci & Engn Coimbatore Tamilnadu India Saranathan Coll Engn Comp Sci & Engn Tiruchirappalli Tamilnadu India M Kumarasamy Coll Engn Dept Comp Sci & Engn Thalavapalayam Tamilnadu India K Ramakrishnan Coll Engn Dept Comp Sci & Engn Samayapuram Kariyamanickam Rd Trichy Tamilnadu India

big data is turning out to be well-liked and enviable amongst numerous users for storing, analyzing, and handling larger quantities of data. Nevertheless, clustering these larger data has turned into more multifaceted owing to its data size. A number of machine learning (ML) approaches have been developed recently to extract information from big data. These existing techniques, on the other hand, do not meet the accuracy criterion. Proposed LDA, PCA, and LSR-based features are first calculated. The optimal features are then chosen using a new SSI-CSA model. These optimal features are then classified via ensemble classifier (EC) that includes SVM, RF, DT and NN and the precise classified outcomes are obtained. This work employs the Shark Smell Integrated Cat Swarm Algorithm (SSI-CSA) model for precise feature selection. In the end, the improvement of deployed scheme is confirmed regarding diverse metrics like FNR, MCC, and accuracy and so on.

关键词： big data classification Proposed LDA mode Ensemble classifier SVM SSI-CSA optimization

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Optimal Feature Selection for big data classification: Firefly with Lion-Assisted Model

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big data 2020年第2期8卷 125-146页

作者： Selvi, Ramar Senthamil Valarmathi, Muniyappan Lakshapalam Saranathan Coll Engn Tiruchirappalli 620012 Tamil Nadu India Dr Mahalingam Coll Engn & Technol Pollachi India

In this article, the proposed method develops a big data classification model with the aid of intelligent techniques. Here, the Parallel Pool Map reduce Framework is used for handling big data. The model involves three main phases, namely (1) feature extraction, (2) optimal feature selection, and (3) classification. For feature extraction, the well-known feature extraction techniques such as principle component analysis, linear discriminate analysis, and linear square regression are used. Since the length of feature vector tends to be high, the choice of the optimal features is complex task. Hence, the proposed model utilizes the optimal feature selection technology referred as Lion-based Firefly (L-FF) algorithm to select the optimal features. The main objective of this article is projected on minimizing the correlation between the selected features. It results in providing diverse information regarding the different classes of data. Once, the optimal features are selected, the classification algorithm called neural network (NN) is adopted, which effectively classify the data in an effective manner with the selected features. Furthermore, the proposed L-FF+NN model is compared with the traditional methods and proves the effectiveness over other methods. Experimental analysis shows that the proposed L-FF+NN model is 92%, 28%, 87%, 82%, and 78% superior to the state-of-art models such as GA+NN, FF+NN, PSO+NN, ABC+NN, and LA+NN, respectively.

关键词： big data classification feature classification Lion with Firefly algorithm optimal feature selection performance measures

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Feature selection based on a crow search algorithm for big data classification

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CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS 2021年 212卷 104288-104288页

作者： Al-Thanoon, Niam Abdulmunim Algamal, Zakariya Yahya Qasim, Omar Saber Univ Mosul Dept Operat Res & Intelligent Tech Mosul Iraq Univ Mosul Dept Stat & Informat Mosul Iraq Univ Mosul Dept Math Mosul Iraq

The common issues of big data are that many of the features may not be relevant. Feature selection has been proven to be an effective way to improve the results of many classification algorithms. Binary crow search algorithm (BCSA) has been developed by getting inspired from natural phenomena to perform feature selection. In BCSA, the flight length parameter plays an important role in the performance of this algorithm. To improve the classification performance with reasonably selected features, an improvement of determining the flight length parameter by employing the concept of opposition-based learning strategy of BCSA is proposed. Experimental results on two datasets show the proposed algorithm, OBL-BCSA, has an advantage over the traditional BCSA in terms of selecting relevant features with a high classification performance. Further, the performance of the OBLBCSA is compared with other algorithms in term of the computational time efficiency which is revealing that OBLBCSA outperforms them.

关键词： Crow search algorithm Opposition-based learning Feature selection big data classification

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Integrating Cuckoo search-Grey wolf optimization and Correlative Naive Bayes classifier with Map Reduce model for big data classification

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data & KNOWLEDGE ENGINEERING 2020年 127卷 101788-101788页

作者： Banchhor, Chitrakant Srinivasu, N. Koneru Lakshmaiah Educ Fdn Dept Comp Sci & Engn Vaddeswaram AP India

big data is progressively being used in various areas, such as industry, financial dealing, medicine, and so on, as it can handle the challenges in processing large amounts of data. One of the data mining techniques used widely and effectively to classify big data is the MapReduce model. In this paper, an approach for the classification of big data is developed using Cuckoo-Grey wolf based Correlative Naive Bayes classifier and MapReduce Model (CGCNB-MRM). Accordingly, a novel classifier, named Cuckoo-Grey wolf based Correlative Naive Bayes classifier (CG-CNB), is designed by modifying CNB classifier with a newly developed optimization algorithm, Cuckoo-Grey Wolf based Optimization (CGWO). CGWO algorithm is designed by the effective integration of Cuckoo Search (CS) Algorithm into Grey Wolf Optimizer (GWO), to optimize the CNB model by the optimal selection of the model parameters. Finally, the proposed CGCNB-MRM approach performs the classification for each data samples based on the probability index table and the posterior probability of the data. Three metrics, such as accuracy, sensitivity, and specificity, are utilized for the performance evaluation of the proposed CGCNB-MRM approach, where it could achieve 80.7% accuracy with 84.5% sensitivity and 76.9% specificity and thus, prove its effectiveness in big data classification.

关键词： big data classification CNB classifier GWO CS algorithm MapReduce model

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