Multi-objective approaches for multi-target learning

Multi-target datasets (MTD) require simultaneous prediction of several variables hence they are considered to be more challenging in terms of predictive tasks compared to single-target datasets. Mining of MTD requires handling of several problems. To exemplify, scale inconsistencies are widely encountered in the targets. Most of the existing approaches resolve this issue by transforming the targets to the same scale, yet those operations may change the statistical properties of the dataset. Besides, features' scale inconsistencies cause problems in semi-supervised learning (SSL) applications since distance-based calculations are required therein. Another issue with MTD is, to explore alternative ways of including the target relations in learning applications. In this thesis, I develop supervised learning (SL), SSL and feature ranking (FR) models for MTD to deal with aforementioned problems. Benefiting from multi-objective optimization concepts, I aim to propose learning strategies that are robust to the type of the variables processed and utilize the target relations at the same time. Specifically, I propose a multi-objective extension for standard decision trees and a selective classifier chaining strategy for SL tasks. Experimental studies show that proposed models outperform their benchmark models. Besides, multi-objective trees extended to their semi-supervised version so that proposed form could result a competitive performance when the label information is not adequate. Performed experiments show a significant improvement of the proposed model over its benchmarks. In addition, since high-dimesionality and irrelevance in features reduce the effectiveness of a learning model, an embedded feature ranking (FR) procedure to semi-supervised trees is given to address this problem. Applications on several datasets show that, proposed FR procedure enhances the predictive performance compared to its benchmark approaches.