An Adaptive Cost-Sensitive Learning and Recursive Denoising Framework for Imbalanced SVM Classification

2024-03-13

AI Summary

To address the unreliability in minority-class recognition, model bias, and noise sensitivity caused by class imbalance in sentiment classification, this paper proposes a robust SVM framework. Methodologically, it introduces a dual-module architecture integrating adaptive cost-sensitive learning with recursive denoising. It pioneers a boundary-aware optimization strategy based on dynamic kernel distance and incorporates a minority-class neighborhood-driven noise identification mechanism, combining k-nearest-neighbor-based noise detection, stochastic optimization, and scalable ensemble techniques. Evaluated across multiple benchmark datasets and sentiment tasks with varying imbalance ratios, the framework achieves significant improvements in Accuracy, G-mean, Recall, and F1-score—consistently outperforming standard SVMs as well as mainstream resampling and cost-sensitive approaches. The work offers both theoretical novelty—particularly in boundary-aware kernel adaptation and noise-resilient minority-class modeling—and practical efficacy for imbalanced sentiment analysis.

Technology Category

• Machine Learning: Kernel Methods
• Natural Language Processing: Text Classification & Sentiment Analysis
• Data Mining & Knowledge Management: Anomaly/Outlier Detection

Application Category

• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

Category imbalance is one of the most popular and important issues in the domain of classification. Emotion classification model trained on imbalanced datasets easily leads to unreliable prediction. The traditional machine learning method tends to favor the majority class, which leads to the lack of minority class information in the model. Moreover, most existing models will produce abnormal sensitivity issues or performance degradation. We propose a robust learning algorithm based on adaptive cost-sensitivity and recursive denoising, which is a generalized framework and can be incorporated into most stochastic optimization algorithms. The proposed method uses the dynamic kernel distance optimization model between the sample and the decision boundary, which makes full use of the sample's prior information. In addition, we also put forward an effective method to filter noise, the main idea of which is to judge the noise by finding the nearest neighbors of the minority class. In order to evaluate the strength of the proposed method, we not only carry out experiments on standard datasets but also apply it to emotional classification problems with different imbalance rates (IR). Experimental results show that the proposed general framework is superior to traditional methods in Accuracy, G-mean, Recall and F1-score.

Problem

• Imbalanced Data
• Classification Accuracy
• Minority Class

Innovation

• Imbalanced Learning
• Cost-sensitive Optimization
• Noise Filtering