Fuzzy Rough Choquet Distances for Classification
This work addresses the need for improved distance metrics in machine learning, particularly for classification tasks like k-nearest neighbors, but it appears incremental as it builds on existing fuzzy rough set and Choquet integral methods.
The paper tackles the problem of capturing non-linear relationships in data for distance-based classification by introducing a novel Choquet distance that integrates fuzzy rough set measures, resulting in a more flexible and accurate distance measure.
This paper introduces a novel Choquet distance using fuzzy rough set based measures. The proposed distance measure combines the attribute information received from fuzzy rough set theory with the flexibility of the Choquet integral. This approach is designed to adeptly capture non-linear relationships within the data, acknowledging the interplay of the conditional attributes towards the decision attribute and resulting in a more flexible and accurate distance. We explore its application in the context of machine learning, with a specific emphasis on distance-based classification approaches (e.g. k-nearest neighbours). The paper examines two fuzzy rough set based measures that are based on the positive region. Moreover, we explore two procedures for monotonizing the measures derived from fuzzy rough set theory, making them suitable for use with the Choquet integral, and investigate their differences.