Learning with partially separable data
This addresses a classification challenge for datasets with partial separability, such as in autism screening, but appears incremental as it builds on clustering and subgroup detection methods.
The paper tackles the problem of classifying partially separable data, where only parts of the data are informative, by proposing a framework and iterative clustering algorithm to detect separable subgroups for classification. It demonstrated capability on an autism screening dataset by distinguishing children with autism from normal ones, while other methods failed.
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.