THE COMPLEX DATA DIMENSIONALITY REDUCTION FOR DIAGNOSTIC AND RECOGNITION MODEL BUILDING ON PRECEDENTS

S. A. Subbotin

Abstract


The problem of data dimensionality reduction for diagnostic and recognizing model construction is solved. The object of study is the
process of data-driven diagnosis. The subject of study is the data reduction methods for diagnostic model construction on precedents. The
purpose of work is to create a set of indicators to quantify the importance of instances and features, as well as a method of data sample dimensionality reduction in the diagnosis and pattern recognition and problem solving. The mathematical support for the sample formation and feature selection is developed on the base of common approach to the assessment of their significance. The set of indicators is proposed to quantify the individual informativity of instances and features in the local neighborhood in the feature space. The exhaustive search methods for data sample dimensionality reduction in the solution of recognition and diagnosis problems have been further developed. They are modified by taking into account of the offered individual estimations of informativity of instances and features in the search operators. The proposed methods and indicator complex are implemented as software and studied in the solution of data dimensionality reduction problems. The conducted experiments confirmed the efficiency of the developed mathematical tools and allow to recommend them for use in practice for solving the problems of non-destructive diagnosis and pattern recognition on features.

Keywords


sample, instance, feature, data dimensionality reduction, sampling, feature selection, diagnosis.

References


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DOI: https://doi.org/10.15588/1607-3274-2016-4-9



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