Learning with partially separable data

TL;DR

This paper introduces a novel framework and algorithm for classifying partially separable data, which traditional methods struggle with, demonstrated through autism screening data.

Contribution

It proposes a new framework and iterative clustering algorithm specifically designed for partially separable data types that are not classifiable by standard methods.

Findings

Successfully distinguished children with autism from normal ones

Outperformed existing methods on the autism screening dataset

Demonstrated effectiveness of the framework in real-world data

Abstract

There are partially separable data types that make classification tasks very hard. In other words, only parts of the data are informative meaning that looking at the rest of the data would not give any distinguishable hint for classification. In this situation, the typical assumption of having the whole labeled data as an informative unit set for classification does not work. Consequently, typical classification methods with the mentioned assumption fail in such a situation. In this study, we propose a framework for the classification of partially separable data types that are not classifiable using typical methods. An algorithm based on the framework is proposed that tries to detect separable subgroups of the data using an iterative clustering approach. Then the detected subgroups are used in the classification process. The proposed approach was tested on a real dataset for autism screening and showed its capability by distinguishing children with autism from normal ones, while the other methods failed to do so.