One class support vector machine based non-relevance feedback document retrieval

This paper reports a new document retrieval method using non-relevant documents. From a large data set of documents, we need to find documents that relate to human interesting in as few iterations of human testing or checking as possible. In each iteration, a comparatively small batch of documents is evaluated for relating to the human interesting. We applied active learning techniques based on support vector machine for evaluating successive hatches, which is called relevance feedback. Our proposed approach has been very useful for document retrieval with relevance feedback experimentally. The relevance feedback needs a set of relevant and non-relevant documents to work usefully. However, the initial retrieved documents, which are displayed to a user, sometimes don´t include relevant documents. In order to solve this problem, we propose a new feedback method using information of non-relevant documents only. We named this method non-relevance feedback document retrieval. The non-relevance feedback document retrieval is based on one-class support vector machine. Our experimental results show that this method can retrieve relevant documents using information of non-relevant documents only.