Binary Classification in Unstructured Space With Hypergraph Case-Based Reasoning

TL;DR

This paper introduces a hypergraph-based binary classification algorithm that is flexible across data types, reduces preprocessing needs, and demonstrates strong empirical performance on various datasets.

Contribution

The paper proposes a novel hypergraph case-based reasoning method for binary classification that handles diverse data representations and missing values, with empirical validation against state-of-the-art methods.

Findings

High accuracy on multiple datasets

Robustness to hyperparameter variations

Reduced data preprocessing requirements

Abstract

Binary classification is one of the most common problem in machine learning. It consists in predicting whether a given element belongs to a particular class. In this paper, a new algorithm for binary classification is proposed using a hypergraph representation. The method is agnostic to data representation, can work with multiple data sources or in non-metric spaces, and accommodates with missing values. As a result, it drastically reduces the need for data preprocessing or feature engineering. Each element to be classified is partitioned according to its interactions with the training set. For each class, a seminorm over the training set partition is learnt to represent the distribution of evidence supporting this class. Empirical validation demonstrates its high potential on a wide range of well-known datasets and the results are compared to the state-of-the-art. The time complexity is given and empirically validated. Its robustness with regard to hyperparameter sensitivity is studied and compared to standard classification methods. Finally, the limitation of the model space is discussed, and some potential solutions proposed.