2021/08/19 by Kaan Gökcesu, Kaan Gokcesu, Gokcesu, Kaan +3 · 3 citations
Computer Science · Decision Sciences · #Advanced Bandit Algorithms Research #Computational Complexity (cs.CC) #FOS: Computer and information sciences #Imbalanced Data Classification Techniques #Machine Learning (cs.LG) #Machine Learning and Algorithms #cs.CC #cs.LG
paper · pdf · doi:10.48550/arxiv.2108.08780
arxiv created 2021/08/19 · openalex publication_date 2021/08/19 · arxiv updated 2021/08/20 · openalex created_date 2021/08/30 · openalex updated_date 2026/07/28
In this work, we aim to calibrate the score outputs of an estimator for the binary classification problem by finding an 'optimal' mapping to class probabilities, where the 'optimal' mapping is in the sense that minimizes the classification error (or equivalently, maximizes the accuracy). We show that for the given target variables and the score outputs of an estimator, an 'optimal' soft mapping, which monotonically maps the score values to probabilities, is a hard mapping that maps the score values to 0 and 1. We show that for class weighted (where the accuracy for one class is more important) and sample weighted (where the samples' accurate classifications are not equally important) errors, or even general linear losses; this hard mapping characteristic is preserved. We propose a sequential recursive merger approach, which produces an 'optimal' hard mapping (for the observed samples so far) sequentially with each incoming new sample. Our approach has a logarithmic in sample size time complexity, which is optimally efficient.