Label Correction Strategy on Hierarchical Multi-Label Classification

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原文链接

NETL

外文摘要：One of the most popular approaches to solve hierarchical multi-label classification problem is to induce Support Vector Machine (SVM) for each class in the hierarchy independently and employ them in a top-down fashion。 This approach always suffers from error propagation and yields such a poor performance of classifiers at the lower levels since no label correlation is considered during the construction。 In this paper， we present a novel method called "label correction"， which takes label correlation into consideration and corrects the results of unusual prediction patterns。 In the experiment， our method does not only improve prediction accuracy on data in hierarchical domains， but it also contributes such a significant impact on data in multi-label domains。

外文关键词：

Hierarchical Multi-Label ClassificationMulti-Label ClassificationLabel CorrelationSupport Vector Machine

作者：

Thanawut Ananpiriyakul、Piyapan Poomsirivilai、Peerapon Vateekul

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作者单位：

Department of Computer Engineering, Chulalongkorn University, Bangkok, Thailand

会议名称：

International conference on machine learning and data mining

会议地点：

St. Petersburg(RU)

会议母体文献：

Machine learning and data mining in pattern recognition

页码：

213-227

出版时间：

2014