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Binary classification in unstructured space with hypergraph case-based reasoning

机译：基于超图案例推理的非结构化空间中的二进制分类

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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. (C) 2019 Elsevier Ltd. All rights reserved.
机译：二进制分类是机器学习中最常见的问题之一。它在于预测给定元素是否属于特定类。本文提出了一种使用超图表示的二进制分类新算法。该方法与数据表示无关，可以使用多个数据源或在非度量空间中使用，并且可以容纳缺少的值。结果，它大大减少了对数据预处理或特征工程的需求。每个要分类的元素根据其与训练集的交互进行划分。对于每个课程，学习训练集分区上的一个半范数来表示支持该课程的证据的分布。经验验证证明了它在各种知名数据集上的巨大潜力，并将结果与​​最新状态进行了比较-艺术。给出时间复杂度并通过经验验证。研究了其在超参数灵敏度方面的鲁棒性，并将其与标准分类方法进行了比较。最后，讨论了模型空间的局限性，并提出了一些可能的解决方案。 （C）2019 Elsevier Ltd.保留所有权利。

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