Safe semi supervised multi-target regression (MTR-SAFER) for new targets learning

Abstract

Multi-target regression (MTR) is a challenging research problem which aims to predict more than one continuous variable as output in a pattern. In recent time, a number of novel applications have increased interest and research in this area. Applications include predicting brain activity from multimedia sensors, different values for stocks from continuous web data, condition of various attributes of the vegetation at a given site, etc. In contrast to conventional regression problems where each instance only belongs to single target from a set of disjoint targets, in multi-target regression, each instance may belong to more than one continuous variable as output. Multi-target regression problems are concerned with problems where there are more than one continuous variables to output. These output variables may or may not be related. A number of approaches have been proposed for this problem. However, for a dynamic multi-target learning system, a pre-trained multi-target system shall be revised as new targets emerge with very few instances. The objective of this paper is to investigate semi-supervised techniques on multi-target regression problems to predict new target using very limited amount of examples. Experiments are then conducted on real world multi-target regression (MTR) data sets. The proposed methodology is then compared with state of the art MTR methods. Promising results are obtained using proposed safe semi-supervised regressor with binary relevance.