M. Kuat, D. Flotzinger, and G. Pfurtscheller. Discovering patterns in EEG-signals: Comparative study of a few methods. In Proc. of the 6th European Conference on Machine Learning, pages 366--371, Heidelberg, 1993. Springer-Verlag. 53  Home/Search   Document Not in Database   Summary   Related Articles   Check

....method is used for selecting a subset of the available attributes with which to build a naive Bayesian classifier. Pazzani (1996) also investigates attribute deletion for naive Bayesian classifiers using the Backward Sequential Elimination (BSE) and FSS approaches (Kittler, 1986) In addition, Kubat, Flotzinger, and Pfurtscheller (1993) show that using decision tree learning as a pre process to select attributes for naive Bayesian classification performs better than either decision tree learning or naive Bayesian classification alone in a domain for discovering patterns in EEG signals. Instead of manipulating the set of ....

Kubat, M., Flotzinger, D., & Pfurtscheller, G. (1993). Discovering patterns in EEG-signals: Comparative study of a few methods. Proceedings of European Conference on Machine Learning (pp. 366-371). Berlin: Springer-Verlag.

....correlated, it might be better to delete one attribute than to assume the two are conditionally independent. They found that an algorithm for feature subset selection (forward sequential selection) improved accuracy on some data sets, but had little or no effect in others. In a related approach, Kubat, Flotzinger, and Pfurtscheller (1993) found that using a decision tree learner to select features for use in the Bayesian classifier gave good results in the domain of EEG signal classification. Kononenko (1991) proposed successively joining dependent attribute values, using a statistical test to judge whether two attribute values ....

Kubat, M., Flotzinger, D., & Pfurtscheller, G. (1993). Discovering patterns in EEG-Signals: Comparative study of a few methods. Proceedings of the Eighth European Conference on Machine Learning (pp. 366--371). Vienna, Austria: Springer-Verlag.

....column refers to the evaluation function used. The forward and backward selection 2 searches start from either no attributes, or a full complement of attributes, and then search for solutions by greedily selecting and adding eliminating attributes to from the attribute subset. Cardie (1993) and Kubat et al. 1993) perform searches by presenting data which includes all the attributes to a decision tree algorithm, and selecting the attributes which appear in the resulting decision tree. The final column, Testing Alg. refers to the learning algorithm that utilised the attribute subset. Authors (System) ....

....the attribute subset. Authors (System) Search Evaluation Testing Alg. Aha Bankert (1994) Beam variants of forward Calinski Harabasz IB1 (BEAM) backward selection separability index Almuallim Dietterich (1991) Breadth first Consistency ID3 (FOCUS) Cardie (1993) C4.5 decision tree kNN CBL Kubat et al. 1993) ID3 decision tree Naive Bayes Liu Setiono (1996) Las Vegas (i.e. Monte Carlo Consistency ID3 (LVF) random sampling) Singh Provan (1996) Forward selection Maximise 1 of 3 Bayesian (Info AS) information metrics Network Table 1: Comparison of different attribute selection studies (filter ....

Kubat, M., Flotzinger, D., and Pfurtscheller, G. (1993). Discovering Patterns in EEG-Signals: Comparative Study of a Few Methods. In Proceedings of the 6th European Conference on Machine Learning, pp. 366--371. Berlin, Heidelberg:Springer-Verlag.

....until it finds a combination consistent with the training data. Although Focus and Relief follow feature selection with decision tree construction, one can of course use other induction methods. For instance, Cardie (1993) uses filtering as a preprocessor for nearest neighbor retrieval, and Kubat, Flotzinger, and Pfurtscheller (1993) filter features for use with a naive Bayesian classifier. Interestingly, both used a decision tree method that relies on an embedded selection scheme as the filter to produce a reduced set of attributes. More recently, Singh and Provan (1996) have used information theoretic metrics to filter ....

Kubat, M., Flotzinger, D., & Pfurtscheller, G. (1993). Discovering patterns in EEG signals: Comparative study of a few methods. Proceedings of the 1993 European Conference on Machine Learning (pp. 367-- 371). Vienna: Springer-Verlag.

....correlated, it might be better to delete one attribute than to assume the two are conditionally independent. They found that an algorithm for feature subset selection (forward sequential selection) improved accuracy on some data sets, but had little or no effect in others. In a related approach, Kubat, Flotzinger, and Pfurtscheller (1993) found that using a decision tree learner to select features for use in the Bayesian classifier gave good results in the domain of EEG signal classification. Kononenko (1991) proposed successively joining dependent attribute values, using a statistical test to judge whether two attribute values ....

Kubat, M., Flotzinger, D., & Pfurtscheller, G. (1993). Discovering patterns in EEG-Signals: Comparative study of a few methods. Proceedings of the Eighth European Conference on Machine Learning (pp. 366--371). Vienna, Austria: Springer-Verlag.

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M. Kuat, D. Flotzinger, and G. Pfurtscheller. Discovering patterns in EEG-signals: Comparative study of a few methods. In Proc. of the 6th European Conference on Machine Learning, pages 366--371, Heidelberg, 1993. Springer-Verlag. 53

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