1-s2.0-S095070511830282X-main (2).pdf

An Empirical Comparison on State-of-the-art Multi-class Imbalance Learning Algorithms and A New Diversified Ensemble Learning Scheme

Abstract

Class-imbalance learning is one of the most challenging problems in machine learning. As a new and important direction in this field, multi-class imbalanced data classification has attracted a great many research focus in recent years. In this paper, we first make a very comprehensive review on state-of-the-art classification algorithms for multi-class imbalanced data. Moreover, we propose a new multi-class imbalance classification algorithm, which is hereafter referred to as the Diversified Error Correcting Output Codes (DECOC) method. The main idea of DECOC is to combine the improved ECOC (Error Correcting Output Codes) method for tackling class imbalance, and the diversified ensemble learning framework, which finds the best classification algorithm (out of many heterogeneous classification algorithms) for each individual sub-dataset resampled from the original data. We conduct experiments on 19 public datasets to empirically compare the performance of DECOC with 17 state-of-the-art multi-class imbalance learning algorithms, using 4 different accuracy measures: overall accuracy, Geometric mean, F-measure, and Area Under Curve. Experimental results demonstrate that DECOC achieves significantly better accuracy performance than the other 17 algorithms on these accuracy metrics. To advance research in this field, we make all the source codes of DECOC and the above-mentioned 17 state-of-the-art algorithms for imbalanced data classification be available at GitHub: https://github.com/chongshengzhang/Multi_Imbalance.

多类不均衡分类算法综述及大规模实验对比分析，刚刚发表在SCI 2区期刊--Knowledge-Based System上，

该论文对比分析了7类、18个多类不均衡数据分类算法的AUC ACC G-Mean F-Measure的值。 

该论文包括了最新了算法和技术。  非常新。更重要的是，本文还提出了一种新的多类不均衡数据分类算法， 基于imECOC+DOVO的思想。

An Empirical Comparison on State-of-the-art Multi-class Imbalance Learning Algorithms and A New Diversified Ensemble Learning Scheme

1-s2.0-S095070511830282X-main (2).pdf

Jingjun BI, Chongsheng ZHANG(*).  An Empirical Comparison on State-of-the-art Multi-class Imbalance Learning Algorithms and A New Diversified Ensemble Learning Scheme.

Knowledge-Based Systems, 2018, 158: 81-93.

https://www.sciencedirect.com/science/article/pii/S095070511830282X

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title = "An empirical comparison on state-of-the-art multi-class imbalance learning algorithms and a new diversified ensemble learning scheme",
journal = "Knowledge-Based Systems",
volume = "158",
pages = "81 - 93",
year = "2018",
issn = "0950-7051",
author = "Jingjun Bi and Chongsheng Zhang",

https://doi.org/10.1016/j.knosys.2018.05.037