Abstract
Learning good-performing classifiers from data with easily separable classes is not usually a difficult task for most of the algorithms. However, problems affecting classifier performance may arise when samples from different classes share similar characteristics or are overlapped, since the boundaries of each class may not be clearly defined. In order to address this problem, the majority of existing works in the literature propose to either adapt well-known algorithms to reduce the negative impact of overlapping or modify the original data by introducing/removing features which decrease the overlapping region. However, these approaches may present some drawbacks: the changes in specific algorithms may not be useful for other methods and modifying the original data can produce variable results depending on data characteristics and the technique used later. An unexplored and interesting research line to deal with the overlapping phenomenon consists of decomposing the problem into several binary subproblems to reduce its complexity, diminishing the negative effects of overlapping. Based on this novel idea in the field of overlapping data, this paper proposes the usage of the One-vs-One (OVO) strategy to alleviate the presence of overlapping, without modifying existing algorithms or data conformations as suggested by previous works. To test the suitability of the OVO approach with overlapping data, and due to the lack of proposals in the specialized literature, this research also introduces a novel scheme to artificially induce overlapping in real-world datasets, which enables us to simulate different types and levels of overlapping among the classes. The results obtained show that the methods using the OVO achieve better performances when considering data with overlapped classes than those dealing with all classes at the same time.
Highlights
In a classification problem a series of input attributes must be linked to a discrete output class [18], [44]
The main aim is to gain a full insight into the influence of overlapping on the classification process and the properties of the OVO decomposition mechanism in such a case
ANALYSIS OF RESULTS OF OVERLAPPING DATA ONLY AFFECTING TRAINING SETS This section assumes a scenario in which overlapping is introduced only in the training sets
Summary
In a classification problem a series of input attributes must be linked to a discrete output class [18], [44] This relationship is established by learning classifiers, which are models built from a set of labeled samples of the problem. In real-world data samples from different classes may share similar attribute values [33]. In these cases, the boundaries of the classes may not be clearly defined, being too complex to be correctly learned. The boundaries of the classes may not be clearly defined, being too complex to be correctly learned This problem is commonly referred as overlapping data [16], [40]. These samples cause uncertainty when determining the decision boundaries and negatively affect classification performance [16]
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