A Deep-Ensemble-Level-Based Interpretable Takagi-Sugeno-Kang Fuzzy Classifier for Imbalanced Data

Recognizing noncoding ribonucleic acid (ncRNA) data is helpful in realizing the regulation of tumor formation and certain aspects of life mechanisms, such as growth, differentiation, development, and immunity. However, the scale of ncRNA data is usually very large. Using machine learning (ML) methods to automatically analyze these data can obtain more precise results than manually analyzing these data, but the traditional ML algorithms can process only small-scale training data. To solve this problem, a novel multitask cross-learning 0-order Takagi–Sugeno–Kang fuzzy classifier (MT-CL-0-TSK-FC) is proposed that uses a multitask cross-learning mechanism to solve the large-scale learning problem of ncRNA data. In addition, the proposed MT-CL-0-TSK-FC method naturally inherits the interpretability of traditional fuzzy systems and eventually generates an interpretable rulesbased database to recognize the ncRNA data. The experimental results indicate that the proposed MT-CL-0TSK-FC method has a faster running time and better classification accuracy than traditional ML methods.

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CONSTRUCTION OF A FUZZY CLASSIFIER STRUCTURE BY COMBINING THE ALGORITHM BASED ON EXTREME VALUES OF FEATURES AND THE SHUFFLED FROG LEAPING ALGORITHM FOR IMBALANCED DATA WITH TWO CLASSES

Автометрия ◽

10.15372/aut20210407 ◽

2021 ◽

Vol 57 (4) ◽

pp. 54-64

Author(s):

M. B. Bardamova ◽

I. A. Hodashinsky

Keyword(s):

Extreme Values ◽

Imbalanced Data ◽

Shuffled Frog Leaping Algorithm ◽

Fuzzy Classifier ◽

Shuffled Frog Leaping

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Interpretable Recognition for Dementia Using Brain Images

Frontiers in Neuroscience ◽

10.3389/fnins.2021.748689 ◽

2021 ◽

Vol 15 ◽

Author(s):

Xinjian Song ◽

Feng Gu ◽

Xiude Wang ◽

Songhua Ma ◽

Li Wang

Keyword(s):

Recognition Performance ◽

Fuzzy Rule ◽

Classification Performance ◽

Fuzzy Rules ◽

High Dimensional ◽

Great Success ◽

Fuzzy Classifier ◽

Brain Images ◽

Fuzzy Classifiers ◽

Takagi Sugeno

Machine learning-based models are widely used for neuroimage-based dementia recognition and achieve great success. However, most models omit the interpretability that is a very important factor regarding the confidence of a model. Takagi–Sugeno–Kang (TSK) fuzzy classifiers as the high interpretability and promising classification performance have widely used in many scenarios. TSK fuzzy classifier can generate interpretable fuzzy rules showing the reasoning process. However, when facing high-dimensional data, the antecedent become complex which may reduce the interpretability. In this study, to keep the antecedent of fuzzy rule concise, we introduce the subspace clustering technique and use it for antecedent learning. Experimental results show that the used model can generate promising recognition performance as well as concise fuzzy rules.

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