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Standard metrics of performance of classifiers, such as accuracy andsensitivity, do not reveal the trust or confidence in the predicted labels ofdata. While other metrics such as the computed probability of a label or thesigned distance from a hyperplane can act as a trust measure, these aresubjected to heuristic thresholds. This paper presents a convexoptimization-based supervised classifier that sequentially partitions a datasetinto several ellipsoids, where each ellipsoid contains nearly all points of thesame label. By stating classification rules based on this partitioning, Bayes'formula is then applied to calculate a trust score to a label assigned to atest datapoint determined from these rules. The proposed Sequential EllipsoidalPartitioning Classifier (SEP-C) exposes dataset irregularities, such as degreeof overlap, without requiring a separate exploratory data analysis. The rulesof classification, which are free of hyperparameters, are also not affected byclass-imbalance, the underlying data distribution, or number of features. SEP-Cdoes not require the use of non-linear kernels when the dataset is not linearlyseparable. The performance, and comparison with other methods, of SEP-C isdemonstrated on the XOR-problem, circle dataset, and other open-sourcedatasets.

Classification with Trust: A Supervised Approach based on Sequential Ellipsoidal Partitioning