# Interclass Interference Suppression in Multi-Class Problems
###### Abstract  
Multi-classifiers are widely applied in many practical problems. But the features that can significantly discriminate a certain class from others are often deleted in the feature selection process of multi-classifiers, which seriously decreases the generalization ability. This paper refers to this phenomenon as interlness interference in multi-class problems and analyzes its reason in detail. Then, this paper summarizes three interlness interference suppression methods including the method based on all-features, one-class classifiers and binary classifiers and compares their effects on interclass interference via the 10-fold cross-validation experiments in 14 UCI datasets. Experiments show that the method based on binary classifiers can suppress the interclass interference efficiently and obtain the best classification accuracy among the three methods. Further experiments were done to compare the suppression effect of two methods based on binary classifiers including the one-versus-one method and one-versus-all method. Results show that the one-versus-one method can obtain a better suppression effect on interclass interference and obtain better classification accuracy. By proposing the concept of interclass inference and studying its suppression methods, this paper significantly improves the generalization ability of multi-classifiers. interclass interference; multi-class classification problem; suppression method; one-versus-all (OVA); one-versus-one (OVO); generalization ability +
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For these classifiers, current studies focus on how to address multi-class problems by decomposing multi-class problems into binary classification problems. When binary classifiers are used to address multi-class problems, the most common strategies are currently one-versus-one (OVO) strategies [35] and one-versus-all (OVA) strategies [36; 37]. For the OVO strategy, [PERSON] et al. [38] developed a distance-based combination strategy, which weights the competence of the outputs of the base classifiers depending on the closeness of the query instance to each one of the classes. [PERSON] et al. 's method reduced the effect of the non-competent classifiers, enhancing the results obtained by the state-of-the-art combinations for the OVO strategy. [PERSON] et al. [39] proposed a dynamic classifier selection strategy for the OVO scheme that tries to avoid non-competent classifiers. For the OVA approach, [PERSON] et al. [40] proved new fast learning rates for OVA multi-class plug-in classifiers trained either from exponentially strongly mixing data or from data generated by a converging drifting distribution. They reported that their results retain the optimal learning rate in the independently identically distributed case in contrast to previous works for least squares SVMs under the binary-class setting. In literature [41], [PERSON] et al. used an ensemble of binary OVA neural network classifiers and reported that the performances of their methods are comparable to lazy learning methods that require the whole dataset. Currently, the generalization ability of multi-classifiers remains a longstanding challenge [42]. Many scholars have carried out relevant research on it. [PERSON] et al. [43] decomposed the classes into subsets by embedding a structure of binary trees and put forward a novel splitting criterion based on minimizing generalization errors and greedy search procedures across the classes. [PERSON] et al. [44] established data-dependent error bounds in terms of complexities of a linear function class defined on a finite set induced by training examples, for which they showed tight lower and upper bounds, applied the results to several prominent multi-class learning machines and exhibited a tight dependency on the number of classes. [PERSON] et al. [45] proposed new methods for support vector machine (SVM) using tree architecture for multi-class classification and reported that their proposed methods run much faster than the traditional techniques but still provide comparable accuracy.