# Interclass Interference Suppression in Multi-Class Problems
###### Abstract
[PERSON] et al. [46] introduced a novel multi-class classification method for the open-set problem and proved the efficiency of their approach in classifying novel instances from known as well as unknown classes through experiments on benchmark datasets and synthetic datasets. From the literature above, it can be noticed that many researchers focus on improving the generalization ability of classifiers. However, these researchers merely focus on generalized classification problems. There is little research specifically for multi-class problems. Researchers usually regard multi-class problems as ordinary classification problems to improve their generalization ability. Few researchers focus on the unique characteristics of multi-class problems. Although there are some studies on how to use binary classifiers to solve multi-class problems, these studies usually focus on the situations where some classifiers cannot solve multi-class problems by themselves. The differences between common binary classifiers and multi-classifiers are not studied sufficiently. This is the problem that this paper deals with. This paper observes that there exists a special phenomenon in multi-class problems, which are quite different from binary classification problems. The features that can significantly discriminate a certain class from others are often deleted in the process of feature selection because they cannot discriminate among other classes. By contrast, the features that can discriminate among all classes are often reserved after feature selection. Although there are small errors in the training set, there may be large errors in test sets because reserved features cannot reflect the essence of a certain class. This phenomenon is an inherent problem in feature selection of multi-class problems and becomes more serious as the number of classes increases. This phenomenon significantly decreases the generalization ability of multi-classifiers. To improve the generalization ability of multi-classifiers better, this phenomenon must be eliminated or suppressed. Therefore, this paper elaborates this special phenomenon in multi-class problems, names it interclass interference, analyzes its reason systematically, designs its suppression methods and compares their suppression effects on interclass interference through 10-fold cross-validation experiments in 14 UCI datasets. The main contributions of this paper are listed as follows. Firstly, this paper observes a special phenomenon in the feature selection of multi-classifiers and names it interclass inference. To the best of our knowledge, this is the first time that the concept of interclass inference is proposed. Secondly, the reasons for interclass inference are analyzed. The essence of interclass inference is revealed in this paper. The negative influence of interclass inference on the generalization ability of multi-class classifiers is also analyzed in detail. Thirdly, this paper summarizes the possible methods for suppressing interclass inference and compares their effects on interclass inference suppression. The rest of this paper is organized as follows. In Section 2, the concept of interclass interference in multi-classifiers is proposed and its reason is analyzed systematically. In Section 3, suppression methods of interclass interference are summarized. In Section 4, interclass interference suppression algorithms used for the comparison experiments in this paper are designed. Section 5 shows the results of our comparison experiments. Section 6 concludes the paper.