基于谱聚类和成对数据表示的多层感知机分类算法

摘要/Abstract

摘要： 面向类别不均衡数据集的分类学习一直是数据挖掘和机器学习领域的研究热点。数据级、算法级和集成方法是目前解决类别不均衡学习的3种主流方法,其中欠抽样是类别不均衡学习一种常用的数据级解决方法,其缺点在于容易丢失多数类中部分有用信息。文中将谱聚类引入到成对数据表示的多数类欠抽样过程中,首先利用谱聚类方法,对多数类样本进行聚类,根据聚类簇大小和簇内样本点与少数类样本点的平均距离,在每个聚类簇内抽取不同个数有代表性的样本,并将簇内样本点之间及所有少数类样本点两两成对表示,从而有效降低了所有样本成对数据表示中两两组合而导致的数据暴涨问题,同时避免了随机抽样而可能导致的有效信息丢失问题。最后在9组UCI数据集上验证了所提算法的有效性。

关键词: 不均衡学习, 多层感知机, 分类, 谱聚类, 欠抽样

Abstract: Classification learning from imbalanced datasets is always one of hot topics in data mining and machine lear-ning domains.Data-level,algorithm-level and ensemble solutions are three main methods so far for addressing imba-lanced learning.Undersmapling,which is one of data-level solutions,is widely utilized in many imbalanced learning scenarios.However,its drawback is discarding potentially useful majority data instances.In this paper,spectral clustering was introduced to take sample of the majority class instances so as to build simultaneous two sample representation.Firstly,all majority class instances are divided into many different clusters by spectral clustering analysis,different numbers of representative samples are extracted from different clusters according to the size of each cluster and the average distance between the minority class instances are generated simultaneous and each cluster,then two sample representation with the extracted instances are generated simultaneous from clusters and the minority class instances.The proposed method not only alleviates the issue of data explosion in simultaneous two sample representation,but also avoids the loss of useful information in random sampling.Finally,several experiments certificate its validity on nine groups of datasets from UCI.

Key words: Classification, Imbalanced learning, Multilayer perceptron, Spectral clustering, Under-sampling

中图分类号:

TP311

刘树栋, 魏嘉敏. 基于谱聚类和成对数据表示的多层感知机分类算法[J]. 计算机科学, 2019, 46(11A): 194-198. https://doi.org/

LIU Shu-dong, WEI Jia-min. Multilayer Perceptron Classification Algorithm Based on Spectral Clusteringand Simultaneous Two Sample Representation[J]. Computer Science, 2019, 46(11A): 194-198. https://doi.org/

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