Decision Functions Model for Metric Methods of Pattern Recognition

Currently, data mining based on machine learning plays a key role in decision support in various industries. An important practical problem of machine learning is the implementation of object classification in real time, which can be achieved by parallelizing data processing algorithms both for input data and for decision function data. To improve the efficiency of parallelizing machine learning methods, a unified decision function model has been developed. The Relevance of this research is to present a unified decision function model in the framework of machine learning algorithms and functions for its parallelization both in terms of input data and decision function data.

The essence of the presented approach is that the features of the operation of metric methods of machine learning are analyzed, independent data for processing are presented using different categories of the analyzed property, developed decision function model describes the object features for input data and decision function data using standardized elements and including functions for their parallel processing based on group parallelization of objects. The proposed approach is based on the use of methods for analyzing algorithms and computational complexity, mathematical statistics and the methodology of designing parallel algorithms.

Experiments have shown that parallelization of the proposed decision function model for the potential function method allows increasing the classification efficiency for one object using additional computing resources, and for a group of objects within the limits of the computer's memory size or planning horizon.

The novelty of the proposed approach is that the model differs from existing ones in a method of formalizing objects and their features using unified elements for training and classification objects and has a structure and functions oriented towards its parallel processing by pattern recognition methods based on decision functions within the framework of group parallelization of objects.

Theoretical significance: the model is unified and can be used to parallelize other pattern recognition methods that can be described by similar parameters, architecture, and classification features.

The practical significance of the proposed approach is that the model allows decomposing the pattern classification problem into separate subtasks of finding regularities between input data and decision function data.

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