Extracting Credible Dependencies for Averaged One-Dependence Estimator Analysis
Of the numerous proposals to improve the accuracy of naive Bayes (NB) by weakening the conditional independence assumption, averaged one-dependence estimator (AODE) demonstrates remarkable zero-one loss performance. However, indiscriminate superparent attributes will bring both considerable computat...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Hindawi Limited
2014-01-01
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| Series: | Mathematical Problems in Engineering |
| Online Access: | http://dx.doi.org/10.1155/2014/470821 |
| Summary: | Of the numerous proposals to improve the accuracy of naive Bayes (NB) by weakening the conditional independence assumption, averaged one-dependence estimator (AODE) demonstrates remarkable zero-one loss performance. However, indiscriminate superparent attributes will bring both considerable computational cost and negative effect on classification accuracy. In this paper, to extract the most credible dependencies we present a new type of seminaive Bayesian operation, which selects superparent attributes by building maximum weighted spanning tree and removes highly correlated children attributes by functional dependency and canonical cover analysis. Our extensive experimental comparison on UCI data sets shows that this operation efficiently identifies possible superparent attributes at training time and eliminates redundant children attributes at classification time. |
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| ISSN: | 1024-123X 1563-5147 |