An extended asymmetric sigmoid with Perceptron (SIGTRON) for imbalanced linear classification

• URL: http://arxiv.org/abs/2312.16043v3
• Date: Tue, 30 Apr 2024 00:11:33 GMT
• Title: An extended asymmetric sigmoid with Perceptron (SIGTRON) for imbalanced linear classification
• Authors: Hyenkyun Woo,
• Abstract summary: This article presents a new parameterized sigmoid called SIGTRON, and its companion convex model called SIGTRON-imbalanced classification (SIC) model. In contrast to the conventional $pi$-weighted cost-sensitive learning model, the SIC model does not have an external $pi$-weight on the loss function. We show that the proposed SIC model is more adaptive to variations of the dataset.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This article presents a new polynomial parameterized sigmoid called SIGTRON, which is an extended asymmetric sigmoid with Perceptron, and its companion convex model called SIGTRON-imbalanced classification (SIC) model that employs a virtual SIGTRON-induced convex loss function. In contrast to the conventional $\pi$-weighted cost-sensitive learning model, the SIC model does not have an external $\pi$-weight on the loss function but has internal parameters in the virtual SIGTRON-induced loss function. As a consequence, when the given training dataset is close to the well-balanced condition considering the (scale-)class-imbalance ratio, we show that the proposed SIC model is more adaptive to variations of the dataset, such as the inconsistency of the (scale-)class-imbalance ratio between the training and test datasets. This adaptation is justified by a skewed hyperplane equation, created via linearization of the gradient satisfying $\epsilon$-optimal condition. Additionally, we present a quasi-Newton optimization(L-BFGS) framework for the virtual convex loss by developing an interval-based bisection line search. Empirically, we have observed that the proposed approach outperforms (or is comparable to) $\pi$-weighted convex focal loss and balanced classifier LIBLINEAR(logistic regression, SVM, and L2SVM) in terms of test classification accuracy with $51$ two-class and $67$ multi-class datasets. In binary classification problems, where the scale-class-imbalance ratio of the training dataset is not significant but the inconsistency exists, a group of SIC models with the best test accuracy for each dataset (TOP$1$) outperforms LIBSVM(C-SVC with RBF kernel), a well-known kernel-based classifier.

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