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…
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