Binary Classification With Hypergraph Case-Based Reasoning

Binary classification is one of the most common problem in machine learning. It consists in predicting whether a given element is of a particular class. In this paper, a new algorithm for binary classification is proposed using a hypergraph representation. Each element to be classified is partitioned according to its interactions with the training set. For each class, the total support is calculated as a convex combination of the evidence strength of the element of the partition. The evidence measure is pre-computed using the hypergraph induced by the training set and iteratively adjusted through a training phase. It does not require structured information, each case being represented by a set of agnostic information atoms. Empirical validation demonstrates its high potential on a wide range of well-known datasets and the results are compared to the literature. The time complexity is given and empirically validated. Its capacity to provide good accuracy with few examples is also studied.