On fine-tuning of Autoencoders for Fuzzy rule classifiers
This work addresses the need for more accurate and efficient fuzzy rule classifiers, particularly for domains requiring expert knowledge integration, though it appears incremental as it builds on existing autoencoder and FRC methods.
The paper tackles the problem of improving Fuzzy rule classifiers (FRC) by incorporating autoencoders and introducing four novel fine-tuning strategies, resulting in state-of-the-art accuracies across five benchmark datasets as validated by comparisons with over 15 previous studies.
Recent discoveries in Deep Neural Networks are allowing researchers to tackle some very complex problems such as image classification and audio classification, with improved theoretical and empirical justifications. This paper presents a novel scheme to incorporate the use of autoencoders in Fuzzy rule classifiers (FRC). Autoencoders when stacked can learn the complex non-linear relationships amongst data, and the proposed framework built towards FRC can allow users to input expert knowledge to the system. This paper further introduces four novel fine-tuning strategies for autoencoders to improve the FRC's classification and rule reduction performance. The proposed framework has been tested across five real-world benchmark datasets. Elaborate comparisons with over 15 previous studies, and across 10-fold cross validation performance, suggest that the proposed methods are capable of building FRCs which can provide state of the art accuracies.