Kernel based online learning for imbalance multiclass classification

Ding, Shuya; Mirza, Bilal; Lin, Zhiping; Cao, Jiuwen; Lai, Xiaoping; Nguyên, Tam; Sepúlveda, José

doi:10.1016/j.neucom.2017.02.102

Cited by 63 publications

(19 citation statements)

References 27 publications

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“…ELM and its variants have been widely used in many big data learning applications, where it is easy to find the raw data with imbalanced hierarchical distribution [77], [78]. Zong et al [79] proposed a weighted ELM (W-ELM) to deal with the imbalance problem.…”

Section: B Elm Matrix Operation Optimizationmentioning

confidence: 99%

A Survey of Distributed and Parallel Extreme Learning Machine for Big Data

et al. 2020

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Extreme learning machine (ELM) is characterized by good generalization performance, fast training speed and less human intervention. With the explosion of large amount of data generated on the Internet, the learning algorithm in the single-machine environment cannot meet the huge memory consumption of matrix computing, so the implement of distributed ELM algorithm has gradually become one of the research focuses. In view of the research significance and implementation value of distributed ELM, this paper first introduced the research background of ELM and improved ELM. Secondly, this paper elaborated the implementation method of distributed ELM from the two directions: ensemble and matrix operation. Finally, we summarized the development status of distributed ELM and discussed the future research direction.

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Section: B Elm Matrix Operation Optimizationmentioning

confidence: 99%

A Survey of Distributed and Parallel Extreme Learning Machine for Big Data

et al. 2020

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“…All these variations of online sequential ELM frameworks for CIL problems require explicit feature mapping in the hidden layer. The idea of using kernel based hidden layer neurons for training the data with implicit feature mapping was exploited in [36] and the author presented a new framework called WOS-ELMK. The proposed algorithm works extremely well with multi-class CIL problems by utilizing implicit kernel mapping rather than explicit random feature mapping.…”

Section: P Weighted Online Sequential Extreme Learning Machine Withmentioning

confidence: 99%

An Overview of Sequential Learning Algorithms for Single Hidden Layer Networks: Current Issues & Future Trends

Arshad¹,

Abido²

2020

Preprint

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This paper serves as an overview for sequential learning algorithms for single hidden layer neural nets. Cite as: M. H. Arshad, M. A. Abido. An Overview of Sequential Learning Algorithms for Single Hidden Layer Networks: Current Issues & Future Trends. Abstract: In this paper, a brief survey of the commonly used sequential-learning algorithms used with single hidden layer feed-forward neural networks is presented. A glimpse at the different kinds that are available in the literature up until now, how they have developed throughout the years, and their relative execution is summarized. Most important things to take note of during the designing phase of neural networks are its complexity, computational efficiency, maximum training time, and ability to generalize the under-study problem. The comparison of different sequential learning algorithms in regard to these merits for single hidden layer neural networks is drawn.

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“…The current recommendations in the field of class lopsidedness learning are outlined beneath; Shuya Ding et al [16] proposed a weighted online consecutive extreme learning machine with bits (WOS-ELMK) for class in adjust getting the hang of utilizing irregular element portion mapping which maintains a strategic distance from the non-ideal shrouded hub issue related class lopsidedness learning. Yang Lu et al [17] proposed a lump based incremental learning strategy called Dynamic Weighted Majority for Imbalance Learning (DWMIL) to manage the information streams with idea float and class awkwardness issue.…”

Section: Literature Reviewmentioning

confidence: 99%

An under sampled k-means approach for handlingimbalanced data using diversified distribution

Shobana¹,

Battula²

2018

IJET

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Some true applications uncover troubles in taking in classifiers from imbalanced information. Albeit a few techniques for enhancing classifiers have been presented, the distinguishing proof of conditions for the effective utilization of the specific strategy is as yet an open research issue. It is likewise worth to think about the idea of imbalanced information, qualities of the minority class dissemination and their impact on arrangement execution. In any case, current investigations on imbalanced information trouble factors have been predominantly finished with manufactured datasets and their decisions are not effortlessly material to this present reality issues, likewise on the grounds that the techniques for their distinguishing proof are not adequately created. In this paper, we recommended a novel approach Under Sampling Utilizing Diversified Distribution (USDD) for explaining the issues of class lopsidedness in genuine datasets by thinking about the systems of recognizable pieces of proof and expulsion of marginal, uncommon and anomalies sub groups utilizing k-implies. USDD utilizes exceptional procedure for recognizable proof of these kinds of cases, which depends on breaking down a class dissemination in a nearby neighborhood of the considered case utilizing k-closest approach. The exploratory outcomes recommend that the proposed USDD approach performs superior to the looked at approach as far as AUC, accuracy, review and f-measure.

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Kernel based online learning for imbalance multiclass classification

Cited by 63 publications

References 27 publications

A Survey of Distributed and Parallel Extreme Learning Machine for Big Data

A Survey of Distributed and Parallel Extreme Learning Machine for Big Data

An Overview of Sequential Learning Algorithms for Single Hidden Layer Networks: Current Issues & Future Trends

An under sampled k-means approach for handlingimbalanced data using diversified distribution

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