Methods of sampling based on exhaustive and evolutionary search

Subbotin, Sergey

doi:10.3103/s0146411613030073

Cited by 24 publications

(10 citation statements)

References 2 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…By analogy to individual informativeness of features widely used in practice [3,6,13] it seems to useful to determine individual informativeness of instance as its influence on determination of class or cluster center and boundaries [20].…”

Section: Individual and Group Instance Informativenessmentioning

confidence: 99%

The sample properties evaluation for pattern recognition and intelligent diagnosis

Subbotin

2014

The 10th International Conference on Digital Technologies 2014

View full text Add to dashboard Cite

The problem of development of indicators characterizing quantitative the training sample properties for the problems of pattern recognition and intelligent diagnosis is solved. It includes such measures as a sample monotonicity, complexity, repetition, relative dimensionality, relative dependence approximation simplicity, relative inconsistency, evenness, class separability and compactness, integrated criteria of sample quality evaluation, sample and feature selection criteria. The using of offered criterions in practice allows to automatize the process of a construction, analysis and comparison of neural models for pattern recognition problem.

show abstract

Section: Individual and Group Instance Informativenessmentioning

confidence: 99%

The sample properties evaluation for pattern recognition and intelligent diagnosis

Subbotin

2014

The 10th International Conference on Digital Technologies 2014

View full text Add to dashboard Cite

show abstract

“…Paper [24] considers an important issue of analyzing the quality of classification of sets of decision trees. A possible way to improve the overall categorization quality is the use of ensembles of decision trees, the use of bugging and boosting mechanisms [25,26]. Note that these schemes would provide the necessary accuracy of a classification model only if there is an effective branching criterion [27].…”

Section: Literature Review and Problem Statementmentioning

confidence: 99%

Classification models of flood-related events based on algorithm trees

Povkhan

2020

EEJET

View full text Add to dashboard Cite

“…It is known that the classification tree (LCT/ACT) model structures are characterized by a compactness, on the one side, and by a non-uniform layer filling (sparsity), on the other one, as compared to the regular tree constructions [18][19][20][21][22][23][24]. Here the issues of classification tree construction process using the methods of the branched attribute selection and those of choosing the logic tree synthesis stopping criterion remain relevant [25][26][27][28][29][30][31][32][33][34]. It should be noted that the classification tree concepts do not conflict with the possibility of using as the classification tree attributes (structural vertices) of not only certain attributes of their connecting objects (the generalized attribute idea was considered in Ref.…”

Section: Review Of the Literaturementioning

confidence: 99%

Limited Method for the Case of Algorithmic Classification Tree

Povhan

2020

RIC

View full text Add to dashboard Cite

Context. The general problem of constructing the algorithmic recognition (classification) trees on the basis of a limited method in the artificial intelligence theory has been considered. The object of the present study is a concept of classification tree (a limited method-based algorithmic classification tree). The relevant methods, algorithms and schemes (a limited method) of constructing the algorithmic classification trees are the subject of this study.Objective. The goal of this work is to develop a simple and efficient limited method of constructing the tree-like recognition and classification models on the basis of the algorithmic classification trees for training selection of a large-volume discrete information that is characterized by a structure of classification trees obtained from independent recognition algorithms assessed in accordance with their general efficiency calculation functional for a wide class of applied tasks.Method. A limited method of constructing the algorithmic classification tree is suggested that constructs a tree-like structure for the preset initial training selection (an ACT model) consisting of a set of autonomous classification/recognition algorithms assessed at each ACT construction step (stage) in accordance with the initial classification. In other words, the limited method of constructing the algorithmic classification tree is suggested, and its idea is a step-by-step approximation of the arbitrary volume/structure selection by a set of independent classification/recognition algorithms. This method provides formation of a current algorithmic tree vertex (node, the generalized ACT attribute) with the selection of the most efficient (high-quality) autonomous classification algorithms from the initial set and construction completion of only those ACT structure paths, where the largest number of classification errors occurs. Such approach at constructing the resultant classification tree (the ACT model) allows the tree size and complexity (i.e. the total number of transitions, structure vertices and layers) to be reduced considerably, the quality of the next analysis (interpretability) and the possibility of decomposition to be increased as well as the ACT structures to be built given the limited hardware resources. The above limited method of constructing the algorithmic classification tree enables one to construct diverse tree-like recognition models with a preset accuracy for a wide class of the artificial intelligence theory tasks.Results. The limited method of constructing the algorithmic classification tree developed and presented in this work has software realization and was investigated and compared to the logical classification tree methods (on the basis of elementary attribute set selection) and the algorithmic tree classification methods (first and second-type ones) when solving the task of real geological data recognition.Conclusions. The experiments carried out in the present work have proved the performance capabilities of the software suggested and demonstrate t...

show abstract

Methods of sampling based on exhaustive and evolutionary search

Cited by 24 publications

References 2 publications

The sample properties evaluation for pattern recognition and intelligent diagnosis

The sample properties evaluation for pattern recognition and intelligent diagnosis

Classification models of flood-related events based on algorithm trees

Limited Method for the Case of Algorithmic Classification Tree

Contact Info

Product

Resources

About