A Fuzzy Similarity Based Classification with Archimedean-Dombi Aggregation Operator

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Abstract
The term "classification" refers to a supervised learning technique in which samples are given class labels based on predetermined classes. Fuzzy classifiers are renowned for their ability to address the issue of outliers and deliver the performance resilience that is much needed. The major goal of this study is to provide a classification algorithm that is effective and accurate. In this work, we address Archimedean-Dombi aggregation operator by extending the similarity classifier. Earlier, Dombi operators were used to study the similarity classifier. We focus on the application of Archimedean-Dombi operators during the classifier's aggregate similarity calculation. Since Archimedean and Dombi operators are well-known for offering appropriate generalization and flexibility respectively in aggregating data, so a different version of the similarity classifier is created. One real-world medical dataset, namely Parkinson disease data set is used to test the proposed approaches. When compared to older existing operators, the new classifiers have better classification accuracy.

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