BPSO-Adaboost-KNN ensemble learning algorithm for multi-class imbalanced data classification

Guo, Hao; Yijing, Li; Li, Yanan; Liu, Xiao; Li, Jinling

doi:10.1016/j.engappai.2015.09.011

Cited by 142 publications

(47 citation statements)

References 48 publications

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“…This case study may be due to rarity of occurrence of a given concept, or even because of some restrictions during the gathering of data for a particular class. In this sense, class imbalance is ubiquitous and prevalent in several applications such as microarray research [6], medical diagnosis [7], oil-bearing of reservoir recognition [8], or intrusion detection systems [9].…”

Section: Introductionmentioning

confidence: 99%

An insight into imbalanced Big Data classification: outcomes and challenges

Fernández

Río

Chawla

et al. 2017

Complex Intell. Syst.

180

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Big Data applications are emerging during the last years, and researchers from many disciplines are aware of the high advantages related to the knowledge extraction from this type of problem. However, traditional learning approaches cannot be directly applied due to scalability issues. To overcome this issue, the MapReduce framework has arisen as a "de facto" solution. Basically, it carries out a "divide-andconquer" distributed procedure in a fault-tolerant way to adapt for commodity hardware. Being still a recent discipline, few research has been conducted on imbalanced classification for Big Data. The reasons behind this are mainly the difficulties in adapting standard techniques to the MapReduce programming style. Additionally, inner problems of imbalanced data, namely lack of data and small disjuncts, are accentuated during the data partitioning to fit the MapReduce programming style. This paper is designed under three main pillars. First, to present the first outcomes for imbalanced classification in Big Data problems, introducing the current

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Section: Introductionmentioning

confidence: 99%

An insight into imbalanced Big Data classification: outcomes and challenges

Fernández

Río

Chawla

et al. 2017

Complex Intell. Syst.

180

View full text Add to dashboard Cite

show abstract

“…At this time, PSO is only deemed as a black box. 50 Based on the above considerations, this article focuses on the combination of feature optimization and PSO, and proposes a feature correlation-based method to improve the search efficiency of PSO. This method increases the probability of feature with more information being selected and effectively selects features with larger discriminative power between classes, so as to reduce high computational cost and improve poorer performance of classification algorithms caused by redundant features.…”

Section: Feature Selection Algorithmmentioning

confidence: 99%

A correlation-based binary particle swarm optimization method for feature selection in human activity recognition

et al. 2018

International Journal of Distributed Sensor Networks

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Effective feature selection determines the efficiency and accuracy of a learning process, which is essential in human activity recognition. In existing works, for simplification purposes, feature selection algorithms are mostly based on the assumption of feature independence. However, in some scenarios, the optimization method based on this independence hypothesis results in poor recognition performance. This article proposes a correlation-based binary particle swarm optimization method for feature selection in human activity recognition. In the proposed algorithm, the particle swarm optimization algorithm is no longer used as a black box. Meanwhile, correlation coefficients among the features are added to binary particle swarm optimization as a feature correlation factor to determine the position of particles, so that the feature with more information is more likely to be selected. The k-nearest neighbor classifier is then used as the fitness function in the particle swarm optimization to evaluate the performance of the feature subset, that is, feature combination with the highest k-nearest neighbor classifier recognition rate would be picked as the eigenvector. Experimental results show that the proposed method can work well with six classifiers, namely, J48, random forest, knearest neighbor, multilayer perceptron, naïve Bayesian, and support vector machine, and the new algorithm can improve the classification accuracy in the OPPORTUNITY Activity Recognition dataset.

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“…Multi class imbalanced data classification problem was solved using binary particle swarm optimization algorithm and KNearest neighbour algorithm 16 . To test the efficiency of an algorithm nineteen benchmarks were considered and applied the algorithm.…”

Section: Related Workmentioning

confidence: 99%

Diabetes Data Classification Using Whale Optimization Algorithm and Backpropagation Neural Network

Rajeshkumar¹,

Kousalya²

2017

Int. Res. J. Pharm.

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Diabetes also called as diabetes mellitus is a health issue which affects more people in a world. Diagnosis of diabetic problem depends on different parameters and requires experience or good algorithm to classify it optimally. Many researchers have found different classification algorithms to diagnose this health issue with promising results. In this paper, combinations of whale optimization algorithm and backpropagation neural network methodology are integrated to diagnose diabetes mellitus. This proposed method supports high convergence speed and improved accuracy. Due to this combination, local minima trapping problem which affects the quality of the solution is totally reduced. In the proposed methodology, Whale optimization technique develops new solutions in solution space and backpropagation algorithm finds the globally optimal solution. Experimental analysis compares the proposed methodology with other algorithms and finally concludes the proposed algorithm outperforms other methodologies.

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BPSO-Adaboost-KNN ensemble learning algorithm for multi-class imbalanced data classification

Cited by 142 publications

References 48 publications

An insight into imbalanced Big Data classification: outcomes and challenges

An insight into imbalanced Big Data classification: outcomes and challenges

A correlation-based binary particle swarm optimization method for feature selection in human activity recognition

Diabetes Data Classification Using Whale Optimization Algorithm and Backpropagation Neural Network

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