ABSTRACT

This paper revisits the problem of active learning and decision making when the cost of labeling incurs cost and unlabeled data is available in abundance. In many real world applications large amounts of data are available but the cost of correctly labeling it prohibits its use. In many cases, where unlabeled data is available in abundance, active learning can be employed. In our proposed approach we will try to incorporate clustering into active learning algorithm and also data reduction is achieved through feature selection. The algorithm learns itself incrementally and will adjust clusters and select appropriate features as it explores more data points.