Using the symmetrical Tau criterion for feature selection decision tree and neural network learning
The data collected for various domain purposes usually contains some features irrelevant tothe concept being learned. The presence of these features interferes with the learning mechanism and as a result the predicted models tend to be more complex and less accurate. It is important to employ an eff...
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| Format: | Conference Paper |
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ACM
2006
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| Online Access: | http://hdl.handle.net/20.500.11937/31352 |
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curtin-20.500.11937-31352 |
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eprints |
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curtin-20.500.11937-313522017-10-02T02:27:39Z Using the symmetrical Tau criterion for feature selection decision tree and neural network learning Hadzic, Fedja Dillon, Tharam S Huan Liu, Robert Stine and Leonardo Auslender feature selection network pruning rule simplification The data collected for various domain purposes usually contains some features irrelevant tothe concept being learned. The presence of these features interferes with the learning mechanism and as a result the predicted models tend to be more complex and less accurate. It is important to employ an effective feature selection strategy so that only the necessary and significant features will be used to learn the concept at hand. The Symmetrical Tau (t) [13] is a statistical-heuristic measure for the capability of an attribute in predicting the class of another attribute, and it has successfully been used as a feature selection criterion during decision tree construction. In this paper we aim to demonstrate some other ways of effectively using the t criterion to filter out the irrelevant features prior to learning (pre-pruning) and after the learning process (post-pruning). For the pre-pruning approach we perform two experiments, one where the irrelevant features are filtered out according to their t value, and one where we calculate the t criterion for Boolean combinations of features and use the highest t-valued combination. In the post-pruning approach we use the t criterion to prune a trained neural network and thereby obtain a more accurate and simple rule set. The experiments are performed on data characterized by continuous and categorical attributes and the effectiveness of the proposed techniques is demonstrated by comparing the derived knowledge models in terms of complexity and accuracy. 2006 Conference Paper http://hdl.handle.net/20.500.11937/31352 ACM fulltext |
| repository_type |
Digital Repository |
| institution_category |
Local University |
| institution |
Curtin University Malaysia |
| building |
Curtin Institutional Repository |
| collection |
Online Access |
| topic |
feature selection network pruning rule simplification |
| spellingShingle |
feature selection network pruning rule simplification Hadzic, Fedja Dillon, Tharam S Using the symmetrical Tau criterion for feature selection decision tree and neural network learning |
| description |
The data collected for various domain purposes usually contains some features irrelevant tothe concept being learned. The presence of these features interferes with the learning mechanism and as a result the predicted models tend to be more complex and less accurate. It is important to employ an effective feature selection strategy so that only the necessary and significant features will be used to learn the concept at hand. The Symmetrical Tau (t) [13] is a statistical-heuristic measure for the capability of an attribute in predicting the class of another attribute, and it has successfully been used as a feature selection criterion during decision tree construction. In this paper we aim to demonstrate some other ways of effectively using the t criterion to filter out the irrelevant features prior to learning (pre-pruning) and after the learning process (post-pruning). For the pre-pruning approach we perform two experiments, one where the irrelevant features are filtered out according to their t value, and one where we calculate the t criterion for Boolean combinations of features and use the highest t-valued combination. In the post-pruning approach we use the t criterion to prune a trained neural network and thereby obtain a more accurate and simple rule set. The experiments are performed on data characterized by continuous and categorical attributes and the effectiveness of the proposed techniques is demonstrated by comparing the derived knowledge models in terms of complexity and accuracy. |
| author2 |
Huan Liu, Robert Stine and Leonardo Auslender |
| author_facet |
Huan Liu, Robert Stine and Leonardo Auslender Hadzic, Fedja Dillon, Tharam S |
| format |
Conference Paper |
| author |
Hadzic, Fedja Dillon, Tharam S |
| author_sort |
Hadzic, Fedja |
| title |
Using the symmetrical Tau criterion for feature selection decision tree and neural network learning |
| title_short |
Using the symmetrical Tau criterion for feature selection decision tree and neural network learning |
| title_full |
Using the symmetrical Tau criterion for feature selection decision tree and neural network learning |
| title_fullStr |
Using the symmetrical Tau criterion for feature selection decision tree and neural network learning |
| title_full_unstemmed |
Using the symmetrical Tau criterion for feature selection decision tree and neural network learning |
| title_sort |
using the symmetrical tau criterion for feature selection decision tree and neural network learning |
| publisher |
ACM |
| publishDate |
2006 |
| url |
http://hdl.handle.net/20.500.11937/31352 |
| first_indexed |
2018-09-06T21:44:39Z |
| last_indexed |
2018-09-06T21:44:39Z |
| _version_ |
1610896144501571584 |