SVM with a neutral class

Abstract

a learning framework. For instance, in the semi-supervised learning, the classifier is allowed to use unlabeled data from underlying classes for improving its classification accuracy [5,28]. In universum learning, we might use unlabeled data samples that do not belong to either classes [29,32]. Integrating pre-defined additional information into a learning framework would usually yield improvement in the classification results and obtaining better insight into data.In this paper, we support the above hypothesis and show that neutral instances can be easily handled with a use of Tri-Class SVM model [2]. Our motivation of including neutral class in a training comes from cheminformatics and computer-aided drug design, in which we focus on detecting compounds acting on a particular protein (biological receptor). A compound is considered active if its binding constant K i ∈ [0, +∞) (measured in a laboratory) is lower than a threshold a = 10 2 , while for inactive compounds a binding constant must be greater than b = 10 31 [31]. Consequently, we get a third class of compounds with an intermediate activity level such that K i ∈ [10 2 , 10 3 ], which forms a neutral class. Although it is a common practice to ignore this neutral class in the learning process [26], we show that its use allows to explore the chemical space better, see Fig. 1.Tri-Class SVM [2] is a generalization of classical SVM [8], which builds a single learning model for three-class problems and avoids pairwise coupling strategy. To use instances of neutral class in the learning process, we develop its two parameterizations: SVM {0} and SVM [−1,1] . In analogy to classical SVM, we look for such a hyperplane which maximizes the margin between positive and negative examples Abstract In many real binary classification problems, in addition to the presence of positive and negative classes, we are also given the examples of third neutral class, i.e., the examples with uncertain or intermediate state between positive and negative. Although it is a common practice to ignore the neutral class in a learning process, its appropriate use can lead to the improvement in classification accuracy. In this paper, to include neutral examples in a training stage, we adapt two variants of Tri-Class SVM (proposed by Angulo et al. in Neural Process Lett 23(1):89-101, 2006), the method designed to solve three-class problems with a use of single learning model. In analogy to classical SVM, we look for such a hyperplane, which maximizes the margin between positive and negative instances and which is localized as close to the neutral class as possible. In addition to original Angulo's paper, we give a new interpretation of the model and show that it can be easily implemented in the primal. Our experiments demonstrate that considered methods obtain better results in binary classification problems than classical SVM and semi-supervised SVM.

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