Uncertain One-Class Learning and Concept Summarization Learning on Uncertain Data Streams

This paper presents a novel framework to uncertain one-class learning and concept summarization learning on uncertain data streams. Our proposed framework consists of two parts. First, we put forward uncertain one-class learning to cope with data of uncertainty. We first propose a local kernel-density-based method to generate a bound score for each instance, which refines the location of the corresponding instance, and then construct an uncertain one-class classifier (UOCC) by incorporating the generated bound score into a one-class SVM-based learning phase. Second, we propose a support vectors (SVs)-based clustering technique to summarize the concept of the user from the history chunks by representing the chunk data using support vectors of the uncertain one-class classifier developed on each chunk, and then extend k-mean clustering method to cluster history chunks into clusters so that we can summarize concept from the history chunks. Our proposed framework explicitly addresses the problem of one-class learning and concept summarization learning on uncertain one-class data streams. Extensive experiments on uncertain data streams demonstrate that our proposed uncertain one-class learning method performs better than others, and our concept summarization method can summarize the evolving interests of the user from the history chunks.