Fusing multiple interval-valued fuzzy monotonic decision trees

Abstract

As a powerful knowledge mining technique for ordinal classification tasks, dominance-based rough set theory has many advantages but also some issues. Sensitivity to noisy information means that even a single mislabeled sample can lead to substantial fluctuations in the approximation calculations. Another issue is that most monotonic classifiers can only handle real-valued data and cannot directly deal with interval-valued data, which is common in practical applications. To address these issues, a tree-based fusion learning method for monotonic classification of interval-valued attributes, named FM-IFMDT, has been proposed. Its functional structure comprises three components: (i) The proposed robust β-precision interval-valued fuzzy dominance neighborhood rough set model (β-IFDNRS) can adaptively identify and filter noise samples. (ii) The interval dominance discernibility matrix based on β-IFDNRS is developed for feature selection and can generate a set of complete and diverse feature subsets. (iii) The novel interval-valued fuzzy monotonic decision tree (IFMDT) based on the probability distribution is trained on each feature subset and is used as the base classifier of the weighted voting fusion model. Extensive experiments show the proposed fusion learning method has significant advantages.