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Classifier Committee Based on Feature Selection Method for Obstructive Nephropathy Diagnosis

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Semantic Methods for Knowledge Management and Communication

Part of the book series: Studies in Computational Intelligence ((SCI,volume 381))

Abstract

The article presents a multiple classifiers approach to the obstructive nephropathy recognition - a disease posing a significant threat to newborns. Nature of the data reflects a problem known as high dimensionality small sample size. In presented approach a feature space division amongst number of classifiers is used to balance the relation between the number of objects and the number of features. Methods of feature selection are apllied for optimum splitting the feature space for classifier ensemble. The optimal size of subspaces and selection of classifier for ensemble is thoroughly tested. Complex performance test are presented to highlight the most efficent tuning of parameters for the presented approach, which is then compared to the classical solutions in this field.

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Author information

Authors and Affiliations

  1. Department of Systems and Computer Networks, Wroclaw University of Technology, Wybrzeze Wyspianskiego 27, 50-370, Wroclaw, Poland

    Bartosz Krawczyk

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  1. Bartosz Krawczyk
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Editor information

Editors and Affiliations

  1. Institute of Informatics, Wrocław University of Technology, Str.Wybrzeże Wyspiańskiego 27, 50-370, Wrocław, Poland

    Radosław Katarzyniak  & Ngoc Thanh Nguyen  & 

  2. Department of Industrial Management & Enterprise Information, Aletheia University, No. 32, Chen-Li Street Tamsui District, New Taipei City, Taiwan, R.O.C.

    Tzu-Fu Chiu

  3. Department of Infomation Management, Aletheia University, No. 32, Chen-Li Street Tamsui District, New Taipei City, Taiwan, R.O.C.

    Chao-Fu Hong

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Krawczyk, B. (2011). Classifier Committee Based on Feature Selection Method for Obstructive Nephropathy Diagnosis. In: Katarzyniak, R., Chiu, TF., Hong, CF., Nguyen, N.T. (eds) Semantic Methods for Knowledge Management and Communication. Studies in Computational Intelligence, vol 381. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23418-7_11

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  • DOI: https://doi.org/10.1007/978-3-642-23418-7_11

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-23417-0

  • Online ISBN: 978-3-642-23418-7

  • eBook Packages: EngineeringEngineering (R0)

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