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Engineering Bulletin # 08, August 2014
УДК: 004.052.42
Method of detecting anomalies in the source data at constructing a prognostic model of a decision tree in decision support systems
Engineering Education # 09, September 2012
DOI: 10.7463/0912.0483269
 The article describes methods of working with noise in the source data at constructing models of data analysis in decision support systems. The paper consists of five parts. The introduction describes the problem of presence of distortions in the input data when decision support systems are in operation. The authors set the task of research and development of methods of analyzing distortions in the data in the decision tree model. The second part of the paper is a survey of existing algorithms of decision trees and methods of work with corrupted data in them. Also the authors reviewed the literature on existing methods for anomaly search in data. The third part describes a method for estimating a local anomaly; the authors propose an extension of this method by using a new formula for calculating the distance between the values ​​of a categorical attribute. In the fourth part the authors propose a method for detection of anomalies in the source data at constructing a predictive model of the decision tree in decision support systems. In the conclusion, the results of the study are listed. 
Prognostic analysis of data by ID3O
Engineering Education # 10, October 2012
DOI: 10.7463/1012.0483286
The article deals with prognostic analysis, namely construction of a prognostic model of the decision tree. The introduction describes principles of the decision tree model and singles out significant problems in algorithms for constructing a decision tree - in particular, the problem of building a decision tree in the presence of noise in the data. The second part describes methods of automatic and manual processing of noise in the data, indicates the problem of limitation of material resources and time resources at manual processing. The third part describes known methods of noise processing in the data and building a decision tree model. On the basis of the research, the authors propose the ID3O algorithm for building a decision tree model in the presence of noise in the raw data and with limited resources for processing and improving data quality. The conclusion presents the results of the proposed algorithm in comparison with existing methods of building a prognostic model of the decision tree.

Engineering Bulletin # 08, August 2012
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