A Classifier Chain Algorithm with K-means for Multi-label Classification on Clouds

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• 作者：Zhilou Yu ; Hong Hao ; Weipin Zhang ; Hongjun Dai
• 关键词：Big data analysis ; Multi ; label classification ; Classifier chain algorithm
• 刊名：Journal of Signal Processing Systems
• 出版年：2017
• 出版时间：March 2017
• 年：2017
• 卷：86
• 期：2-3
• 页码：337-346
• 全文大小：
• 刊物类别：Engineering
• 刊物主题：Signal,Image and Speech Processing; Circuits and Systems; Electrical Engineering; Image Processing and Computer Vision; Pattern Recognition; Computer Imaging, Vision, Pattern Recognition and Graphics;
• 出版者：Springer US
• ISSN：1939-8115
• 卷排序：86

文摘

It has become a basic precursor and facilitator to analyze the emergence of big data with the rise of cloud computing and cloud storage by means of the novel standardized technologies. Then, binary relevance method is carried out as one of the widely known classifier chain methods for multi-label classification. It achieves a higher predictive performance, but it still retains a complex process and takes much computation time. So, in this paper, we present a enhanced classifier chain algorithm with K-means cluster method to confirm the order of the binary classifiers. It has a different strategy that several times of K-means algorithms are employed to get the correlations between labels and to confirm the order of binary classifiers. The algorithm ensures the precise correlations to be transmitted persistently to improve the earlier predictions accuracy. The experiments on a sample data sets of Reuters-21578 show that the approach is effective and appealing in the common cases, it is accurate for a preliminary classification to provide a basis for the further refined classifications.