Abstract
This article proposes a Parallel Neural Fuzzy (PNF) possibilistic classifier model and it is the application in autism assessment systems. An independent neural network and a fuzzy system work in parallel on a set of input and produces individual support (belief) regarding the output classes. The beliefs of heterogeneous classifiers are then fused using a possibilistic classifier to take a joint decision. A neural network is trained with samples to simulate expertise while the fuzzy system is embedded with theoretical knowledge, specific to a problem. This model has been implemented and applied as an assessment support system for grading childhood autism. Application specific observations demonstrate two advantages over an individual neural network classifier: first, an improved accuracy rate or decreased misdiagnosis rate and second, a certain or unique grading than an uncertain or vague grading. The proposed approach can serve as a guide in determining the correct grade of autistic disorder.
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Pratap, A., Kanimozhiselvi, C.S., Vijayakumar, R., Pramod, K.V. (2016). Parallel Neural Fuzzy-Based Joint Classifier Model for Grading Autistic Disorder. In: Balas, V., C. Jain, L., Kovačević, B. (eds) Soft Computing Applications. SOFA 2014. Advances in Intelligent Systems and Computing, vol 356. Springer, Cham. https://doi.org/10.1007/978-3-319-18296-4_2
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DOI: https://doi.org/10.1007/978-3-319-18296-4_2
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