Effective supervised discretization for classification based on correlation maximization

In many real-world applications, there are features (or attributes) that are continuous or numerical in the data. However, many classification models only take nominal features as the inputs. Therefore, it is necessary to apply discretization as a pre-processing step to transform numerical data into nominal data for such models. Well-discretized data should not only characterize the original data to produce a concise summarization, but also improve the classification performance. In this paper, a novel and effective supervised discretization algorithm based on correlation maximization (CM) is proposed by using multiple correspondence analysis (MCA) which is a technique to capture the correlations between multiple variables. For each numeric feature, the correlation information generated from MCA is used to build the discretization algorithm that maximizes the correlations between feature intervals/items and classes. Empirical comparisons with four other commonly used discretization algorithms are conducted using six well-known classifiers. Results on five UCI datasets and five TRECVID datasets demonstrate that our proposed discretization algorithm can automatically generate a better set of features (feature intervals) by maximizing their correlations with the classes and thus improve the classification performance.