Instance-Based Cost-Sensitive Boosting

Many classification algorithms aim to minimize just their training error count; however, it is often desirable to minimize a more general cost metric, where distinct instances have different costs. In this paper, an instance-based cost-sensitive Bayesian consistent version of exponential loss function is proposed. Using the modified loss function, the derivation of instance-based costsensitive extensions of AdaBoost, RealBoost and GentleBoost are developed which are termed as ICSAdaBoost, ICSRealBoost and ICSGentleBoost, respectively. In this research, a new instance-based cost generation method is proposed instead of doing this expensive process by experts. Thus, each sample takes two cost values; a class cost and a sample cost. The first cost is equally assigned to all samples of each class while the second cast is generated according to the probability of each sample within its class probability density function. Experimental results of the proposed schemes imply 12% enhancement in terms of F-measure and 13% on cost-persample over a variety of UCI datasets, compared to the state-of-the-art methods. The significant priority of the proposed method is supported by applying the pair of T-tests to the results.