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Information and distance measures with application to feature evaluation and to heuristic sequential classification Vilmansen, Toomas Rein
Abstract
Two different aspects of the problem of selecting measurements for statistical pattern recognition are investigated. First, the evaluation of features for multiclass recognition problems by using measures of probabilistic dependence is examined. Secondly, the problem of evaluation and selection of features for a general tree type classifier is investigated. Measures of probabilistic dependence are derived from pairwise distance measures such as Bhattacharyya distance, divergence, Matusita's distance, and discrimination information. The properties for the dependence measures are developed in the context of feature class dependency. Inequalities relating the measures are derived. Also upper and lower bounds on error probability are derived for the different measures. Comparisons of the bounds are made. Feature ordering experiments are performed to compare the measures to error probability and to each other. A fairly general tree type sequential classifier is examined. An algorithm which uses distance measures for clustering probability distributions and which uses dependence and distance measures for ordering features is derived for constructing the decision tree. The concept of confidence in a decision in conjunction with backtracking is introduced in order to make decisions at any node of the tree tentative and reversible. Also, the idea of re-introducing classes at any stage is discussed. Experiments are performed to determine the storage and processing requirements of the classifier, to determine effects of various parameters on performance, and to determine the usefulness of procedures for backtracking and reintroducing of classes.
Item Metadata
Title |
Information and distance measures with application to feature evaluation and to heuristic sequential classification
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Creator | |
Publisher |
University of British Columbia
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Date Issued |
1974
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Description |
Two different aspects of the problem of selecting measurements for statistical pattern recognition are investigated. First, the evaluation of features for multiclass recognition problems by using measures of probabilistic dependence is examined. Secondly, the problem of evaluation and selection of features for a general tree type classifier is investigated.
Measures of probabilistic dependence are derived from pairwise distance measures such as Bhattacharyya distance, divergence, Matusita's distance, and discrimination information. The properties for the dependence measures are developed in the context of feature class dependency. Inequalities relating the measures are derived. Also upper and lower bounds on error probability are derived for the different measures. Comparisons of the bounds are made. Feature ordering experiments are performed to compare the measures to error probability and to each other.
A fairly general tree type sequential classifier is examined. An algorithm which uses distance measures for clustering probability distributions and which uses dependence and distance measures for ordering features is derived for constructing the decision tree. The concept of confidence in a decision in conjunction with backtracking is introduced in order to make decisions at any node of the tree tentative and reversible. Also, the idea of re-introducing classes at any stage is discussed. Experiments are performed to determine the storage and processing requirements of the classifier, to determine effects of various parameters on performance, and to determine the usefulness of procedures for backtracking and reintroducing of classes.
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Genre | |
Type | |
Language |
eng
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Date Available |
2010-01-27
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Provider |
Vancouver : University of British Columbia Library
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Rights |
For non-commercial purposes only, such as research, private study and education. Additional conditions apply, see Terms of Use https://open.library.ubc.ca/terms_of_use.
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DOI |
10.14288/1.0093184
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URI | |
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Program | |
Affiliation | |
Degree Grantor |
University of British Columbia
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Campus | |
Scholarly Level |
Graduate
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Aggregated Source Repository |
DSpace
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Item Media
Item Citations and Data
Rights
For non-commercial purposes only, such as research, private study and education. Additional conditions apply, see Terms of Use https://open.library.ubc.ca/terms_of_use.