Fuzzy-Based Machine Learning Algorithm for Intelligent Systems

  • K Pradheep Kumar
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 808)


It has become essential to develop machine learning techniques due to the automation of various tasks. At present, several tasks need manual intervention for better reliability of the system. In this work, fuzzy-based approach has been proposed where systems are trained based on initial data sets. In several data sets, the data is either partially available or unavailable. When data sets need to be used on real time systems, the non-availability of data may lead to catastrophe. In this approach, a fuzzy-based rule set is formulated. The rule strength is used to determine the effectiveness. Rules with similar strengths are clustered together. The learning is carried out by determining a threshold for the formulated rule set. Based on the threshold computed, a modified rule set is formulated with rule strengths greater than the computed threshold. A semi-supervised learning approach that uses an activation function is employed. The fuzzy learning approach proposed in this work reduces the error by 20% compared to conventional approaches.


Threshold Activation function Learning rule matrix Machine learning Rule strength Smart systems Semi-supervised learning 


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© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.BITS PilaniPilaniIndia

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