Multi-Objective PSO-fuzzy Optimization Approach to Improve Interpretability and Accuracy in Medical Data

 
 
 
  • Abstract
  • Keywords
  • References
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  • Abstract


    Today, Decision Support Systems (DSS) plays a significant role in a medical and healthcare domain. Designing an Automatic Fuzzy
    Rule-based Classification Systems (FRBCSs) is considered as optimization problem associated to a result of high interpretability and
    accuracy. Interpretability and accuracy are the two main objectives to be improved in the optimization measurement of FRBCSs. However, improving these objectives is found to be difficult in most of the existing systems due to the conflicting issues between accuracy
    and interpretability. In this work, we proposed an approach that can effectively handle accuracy- interpretability trade-off in constructing
    FRBCSs. We designed automated FRBCSs in the form of Multi-objective Particle Swarm Optimization with Crowding Distance. In the
    approach, there will be a collection of solutions to FRBCSs that deem best global minimum or global maximum with respect to interpretability and accuracy. Our method is evaluated on a popular benchmark data sets being used in a medical domain for evaluations. These
    datasets are Liver Disorders (BUPA), Pima Indians Diabetes and Thyroid Disease (New Thyroid). The result obtained shows that the
    proposed method yields an optimum solution in minimizing the trade-off between accuracy and interpretability. Moreover, the result of
    the comparison shows that our approach outperforms the alternate techniques in terms of accuracy of FRBCSs and also exhibits good
    result in terms of interpretability objective.


  • Keywords


    Fuzzy Rule-based Classification, Multi-objective PSO, Particle Swarm Optimization (PSO), Rule-based Systems, Single-objective PSO

  • References


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Article ID: 29943
 
DOI: 10.14419/ijet.v7i4.31.29943




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