TSK Fuzzy System Towards Few Labeled Incomplete Multi-View Data Classification

TL;DR

This paper introduces a semi-supervised fuzzy system that effectively handles incomplete multi-view data with limited labels, combining view imputation, label learning, and fuzzy modeling for improved interpretability and robustness.

Contribution

It proposes a novel integrated approach for incomplete multi-view data classification that combines missing view imputation, pseudo-label learning, and fuzzy system modeling in a single, efficient process.

Findings

Outperforms state-of-the-art methods on real datasets

Handles incomplete and few labeled multi-view data effectively

Provides interpretable fuzzy rules for decision making

Abstract

Data collected by multiple methods or from multiple sources is called multi-view data. To make full use of the multi-view data, multi-view learning plays an increasingly important role. Traditional multi-view learning methods rely on a large number of labeled and completed multi-view data. However, it is expensive and time-consuming to obtain a large number of labeled multi-view data in real-world applications. Moreover, multi-view data is often incomplete because of data collection failures, self-deficiency, or other reasons. Therefore, we may have to face the problem of fewer labeled and incomplete multi-view data in real application scenarios. In this paper, a transductive semi-supervised incomplete multi-view TSK fuzzy system modeling method (SSIMV_TSK) is proposed to address these challenges. First, in order to alleviate the dependency on labeled data and keep the model interpretable, the proposed method integrates missing view imputation, pseudo label learning of unlabeled data, and fuzzy system modeling into a single process to yield a model with interpretable fuzzy rules. Then, two new mechanisms, i.e. the bidirectional structural preservation of instance and label, as well as the adaptive multiple alignment collaborative learning, are proposed to improve the robustness of the model. The proposed method has the following distinctive characteristics: 1) it can deal with the incomplete and few labeled multi-view data simultaneously; 2) it integrates the missing view imputation and model learning as a single process, which is more efficient than the traditional two-step strategy; 3) attributed to the interpretable fuzzy inference rules, this method is more interpretable. Experimental results on real datasets show that the proposed method significantly outperforms the state-of-the-art methods.