Feature Reduction in Graph Analysis
A common approach to improve medical image classification is to add more features to the classifiers; however, this increases the time required for preprocessing raw data and training the classifiers, and the increase in features is not always beneficial. The number of commonly used features in the...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2008-08-01
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| Series: | Sensors |
| Subjects: | |
| Online Access: | http://www.mdpi.com/1424-8220/8/8/4758/ |
| _version_ | 1798043263865716736 |
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| author | Punpiti Piamsa-nga Rapepun Piriyakul |
| author_facet | Punpiti Piamsa-nga Rapepun Piriyakul |
| author_sort | Punpiti Piamsa-nga |
| collection | DOAJ |
| description | A common approach to improve medical image classification is to add more features to the classifiers; however, this increases the time required for preprocessing raw data and training the classifiers, and the increase in features is not always beneficial. The number of commonly used features in the literature for training of image feature classifiers is over 50. Existing algorithms for selecting a subset of available features for image analysis fail to adequately eliminate redundant features. This paper presents a new selection algorithm based on graph analysis of interactions among features and between features to classifier decision. A modification of path analysis is done by applying regression analysis, multiple logistic and posterior Bayesian inference in order to eliminate features that provide the same contributions. A database of 113 mammograms from the Mammographic Image Analysis Society was used in the experiments. Tested on two classifiers – ANN and logistic regression – cancer detection accuracy (true positive and false-positive rates) using a 13-feature set selected by our algorithm yielded substantially similar accuracy as using a 26-feature set selected by SFS and results using all 50-features. However, the 13-feature greatly reduced the amount of computation needed. |
| first_indexed | 2024-04-11T22:46:56Z |
| format | Article |
| id | doaj.art-22d2d8e3f6824a9a9e7c07c26c10ddeb |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-04-11T22:46:56Z |
| publishDate | 2008-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-22d2d8e3f6824a9a9e7c07c26c10ddeb2022-12-22T03:58:44ZengMDPI AGSensors1424-82202008-08-018847584773Feature Reduction in Graph AnalysisPunpiti Piamsa-ngaRapepun PiriyakulA common approach to improve medical image classification is to add more features to the classifiers; however, this increases the time required for preprocessing raw data and training the classifiers, and the increase in features is not always beneficial. The number of commonly used features in the literature for training of image feature classifiers is over 50. Existing algorithms for selecting a subset of available features for image analysis fail to adequately eliminate redundant features. This paper presents a new selection algorithm based on graph analysis of interactions among features and between features to classifier decision. A modification of path analysis is done by applying regression analysis, multiple logistic and posterior Bayesian inference in order to eliminate features that provide the same contributions. A database of 113 mammograms from the Mammographic Image Analysis Society was used in the experiments. Tested on two classifiers – ANN and logistic regression – cancer detection accuracy (true positive and false-positive rates) using a 13-feature set selected by our algorithm yielded substantially similar accuracy as using a 26-feature set selected by SFS and results using all 50-features. However, the 13-feature greatly reduced the amount of computation needed.http://www.mdpi.com/1424-8220/8/8/4758/Path AnalysisGraph AnalysisFeature SelectionMammogram. |
| spellingShingle | Punpiti Piamsa-nga Rapepun Piriyakul Feature Reduction in Graph Analysis Sensors Path Analysis Graph Analysis Feature Selection Mammogram. |
| title | Feature Reduction in Graph Analysis |
| title_full | Feature Reduction in Graph Analysis |
| title_fullStr | Feature Reduction in Graph Analysis |
| title_full_unstemmed | Feature Reduction in Graph Analysis |
| title_short | Feature Reduction in Graph Analysis |
| title_sort | feature reduction in graph analysis |
| topic | Path Analysis Graph Analysis Feature Selection Mammogram. |
| url | http://www.mdpi.com/1424-8220/8/8/4758/ |
| work_keys_str_mv | AT punpitipiamsanga featurereductioningraphanalysis AT rapepunpiriyakul featurereductioningraphanalysis |