Fuzzy integrated classifier with high interpretability based on parallel learning

Abstract

The invention discloses a fuzzy integrated classifier with high interpretability based on parallel learning. The fuzzy integrated classifier sequentially comprises the following steps of constructing a zero-order TSK fuzzy classifier fastly; constructing an enhanced verification data set, calculating the value of each corresponding output function for each sample of the verification data set, and taking the value of each corresponding output function as an enhanced feature of the original sample so as to form the enhanced verification data set; respectively calling an FCM algorithm for each class on the enhanced verification data set to generate a series of representative center points and corresponding tags thereof, and removing the enhanced features of the representative center points; for any test sample, finding k representative center points closest to the test sample by using the KNN to determine the class label of the test sample. According to the present invention, a parallel learning method is adopted, the operation complexity is low, the structure is simple and easy to understand, the fast learning can be carried out, and the good performance is achieved on the classification problem processing.

Description

【Technical field】 [0001] The invention relates to the technical field of fuzzy recognition and machine learning, in particular to the technical field of fuzzy integration and rapid classification based on parallel learning. 【Background technique】 [0002] In order to improve the classification performance of fuzzy classifiers, various ensemble learning strategies are adopted to integrate multiple fuzzy sub-classifiers to improve the classification accuracy of classification tasks. Typical integration techniques include layered learning and Boosting learning, respectively as figure 2 and image 3 as shown, figure 2 Three types of hierarchical structures are demonstrated, and in existing hierarchical TSK fuzzy classifiers, it is difficult to understand the output of each intermediate TSK fuzzy sub-classifier, which serves as the input of the next layer of TSK fuzzy sub-classifiers. Therefore, due to the existence of intermediate variables, it is difficult for the TSK fuzzy ...

AI Technical Summary

Problems solved by technology

In Boosting ensemble learning, such as image 3 As shown, due to the correlation between TSK fuzzy sub-classifiers, when adding or deleting a TSK fuzzy sub-classifier, Boosting learning must abandon the learned weights of each TSK fuzzy sub-classifier, and then assign new weights to retrain it, resulting in a heavy computational burden

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