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Neuro-Fuzzy-Rough Classification for Improving Efficiency and Performance in Case-Based Reasoning Retrieval

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Computational Network Application Tools for Performance Management

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Abstract

Case-based reasoning (CBR) is an artificial intelligence (AI) technique for solving problems. The very fact that CBR draws on past experiences to solve a new problem makes it intuitively appealing as humans also have the same problem-solving behavior. Another advantage of CBR over other conventional AI techniques is that it can work on a shallow knowledge base to start with. These make CBR an excellent method to solve real-life problems and useful in the field like medical diagnosis, engineering diagnosis, product selection, weather prediction, aerospace applications, etc. Classification plays a vital role in the retrieval of cases, as a correct classification results in a correctly retrieved case, which eventually results in a correct solution given by the CBR system. Typically, case retrieval is similarity-based and uses a k-nearest neighbor (k-NN) algorithm. Retrieval aims to find among the stored cases the best match for a given new case. Typically, CBR systems use the nearest neighbor algorithm as a similarity metric for retrieving cases. In this paper, the researchers use the machine learning workbench WEKA to combine well-known classifiers multilayer perceptron and fuzzy-rough nearest neighbor and compare the performance of k-NN with them. They have used benchmark medical data sets to carry out the evaluation process. The experimental results show that the combination of multilayer perceptron and fuzzy-rough nearest neighbor outperforms k-NN to a significant extent for classification, thus effectively improving the case retrieval efficiency and performance.

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Authors and Affiliations

  1. Department of Computer Science, Assam University, Silchar, Assam, India

    Nabanita Choudhury & Shahin Ara Begum

Authors
  1. Nabanita Choudhury
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  2. Shahin Ara Begum
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Corresponding author

Correspondence to Nabanita Choudhury .

Editor information

Editors and Affiliations

  1. Department of Applied Science and Engineering, Indian Institute of Technology Roorkee, Roorkee, Uttarakhand, India

    Millie Pant

  2. Amity School of Engineering and Technology, Amity University Rajasthan, Jaipur, Rajasthan, India

    Tarun K. Sharma

  3. Department of Computer Science, Czech Technical University in Prague, Ostrava, Prague, Czech Republic

    Sebastián Basterrech

  4. Amity Institute of Information Technology, Amity University Rajasthan, Jaipur, Rajasthan, India

    Chitresh Banerjee

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Choudhury, N., Begum, S.A. (2020). Neuro-Fuzzy-Rough Classification for Improving Efficiency and Performance in Case-Based Reasoning Retrieval. In: Pant, M., Sharma, T., Basterrech, S., Banerjee, C. (eds) Computational Network Application Tools for Performance Management. Asset Analytics. Springer, Singapore. https://doi.org/10.1007/978-981-32-9585-8_4

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