Binary Classification in Unstructured Space With Hypergraph Case-Based Reasoning
This addresses binary classification for scenarios with unstructured or non-metric data, offering a method that is incremental in its approach to handling multiple data sources and missing values.
The paper tackles binary classification in unstructured spaces by proposing a hypergraph-based algorithm that reduces preprocessing needs, and demonstrates its potential with empirical validation on various datasets compared to state-of-the-art methods.
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.