Quality-Based Learning for Web Data Classification

The types of web data vary in terms of information quantity and quality. For example, some pages contain numerous texts, whereas some others contain few texts; some web videos are in high resolution, whereas some other web videos are in low resolution. As a consequence, the quality of extracted features from different web data may also vary greatly. Existing learning algorithms on web data classification usually ignore the variations of information quality or quantity. In this paper, the information quantity and quality of web data are described by quality-related factors such as text length and image quantity, and a new learning method is proposed to train classifiers based on quality-related factors. The method divides training data into subsets according to the clustering results of qualityrelated factors and then trains classifiers by using a multitask learning strategy for each subset. Experimental results indicate that the quality-related factors are useful in web data classification, and the proposed method outperforms conventional algorithms that do not consider information quantity and quality.

Authors

• Ou Wu Tianjin University University of Chinese Academy of Sciences
• Ruiguang Hu Chinese Academy of Sciences
• Xue Mao Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China
• Weiming Hu State Key Laboratory of Multimodal Artificial Intelligence Systems, Institute of Automation, Chinese Academy of Sciences School of Artificial Intelligence, University of Chinese Academy of Sciences School of Information Science and Technology, ShanghaiTech University

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