D. Boyce, A. Farhi, and R Weischedel. Optimal Subset Selection. Springer-Verlag, Berlin, Germany, 1974. 44  Home/Search   Document Not in Database   Summary   Related Articles   Check

.... 2 Feature Subset Selection as a search problem Even if we locate our work as a Machine Learning or Data Mining approach, the FSS literature includes plenty of works in other fields such as Pattern Recognition (Jain and Chandrasekaran [27] Kittler [31] Stearns [61] Statistic (Boyce et al. [7], Miller [43] Narendra and Fukunaga [49] or TextLearning (Mladeni c [44] In the Bayesian network community we have the example of the work of Provan and Singh [55] who, using a Bayesian network as a classifier, build it selecting in a greedy manner the variables that should maximize the ....

D. Boyce, A. Farhi and R. Weischedel, Optimal Subset Selection, Springer-Verlag, Berlin, Germany, 1974.

.... 2 Feature Subset Selection as a search problem Even if we locate our work as a Machine Learning or Data Mining approach, the FSS literature includes plenty of works in other elds such as Pattern Recognition (Jain and Chandrasekaran [27] Kittler [31] Stearns [61] Statistic (Boyce et al. [7], Miller [43] Narendra and Fukunaga [49] or TextLearning (Mladeni c [44] In the Bayesian network community we have the example of the work of Provan and Singh [55] who, using a Bayesian network as a classi er, build it selecting in a greedy manner the variables that should maximize the ....

D. Boyce, A. Farhi and R. Weischedel, Optimal Subset Selection, Springer-Verlag, Berlin, Germany, 1974.

....II. Feature Subset Selection as a search problem Even if our work is located in Machine Learning, FSS literature includes plenty of works in other fields such as Pattern Recognition (Jain and Chandrasekaran [37] Stearns [81] Kittler [41] Statistic (Narendra and Fukunaga [67] Boyce et al. [13], Miller [60] Data Mining (Chen et al. 20] Provost and Kolluri [73] or Text Learning (Mladeni c [61] Yang and Pedersen[87] In this way, different communities have exchanged and shared ideas among them to deal with the FSS problem. As reported by Aha and Bankert [2] the objective of ....

D. Boyce, A. Farhi, R. Weischedel, Optimal Subset Selection, Springer-Verlag, Berlin, Germany, 1974.

....not included in the schema. However, while nearest neighbor algorithms use the nearest neighbor to classify instances, a DTM classifier defaults to the majority whenever the distance is greater than zero. Feature subset selection has been long studied in the statistics community (Miller 1990, Boyce, Farhi Weischedel 1974), in the pattern recognition community (Devijver Kittler 1982) and lately in the machine learning community (John et al. 1994, Moore Lee 1994, Caruana Freitag 1994, Kohavi Frasca 1994, Langley Sage 1994, Aha Bankert 1994) Decision tables have a bias similar to that of oblivious ....

Boyce, D., Farhi, A. & Weischedel, R. (1974), Optimal Subset Selection, SpringerVerlag.

....On the other hand, if the training sample has too many objects, a subsample selection method (Section 2.5.1) can be employed to filter out the unnecessary observations. Feature subset selection There is a large body of work on choosing relevant subsets of features (for example, see the texts [116, 35, 322]) Most of this work was not developed in the context of tree induction, but a lot of it has direct applicability. There are two components to any method that attempts to choose the best subset of features. The first is a metric using which two feature subsets can be compared to determine which is ....

D. Boyce, A. Farhi, and R. Weishedel. Optimal Subset Selection. SpringerVerlag, 1974.

....respect to the full leg study, the variance of intensities in the region, etc. The supervised decision tree methods used for classification [2] have an additional advantage that they indicate which features are the most useful for discrimination. These and traditional feature selection methods [1] indicated that there was no improvement in the classification results by adding more features to the above set. Feature vectors are first computed along each row and column of the image, and then efficiently smoothed over entire regions. 3.3 Supervised classification The next step is to ....

D. Boyce, A. Farhi, and R. Weishedel. Optimal Subset Selection. Springer-Verlag, 1974.

....selection is instance selection. If the training sample is too large to allow for efficient classifier induction, a subsample selection method (Section 5.1.3) can be employed. 5.1.1. Feature subset selection There is a large body of work on choosing relevant subsets of features (see the texts [84, 27, 245]) Much of this work was not developed in the context of tree induction, but a lot of it has direct applicability. There are two components to any method that attempts to choose the best subset of features. The first is a metric using which two feature subsets can be compared to determine which is ....

D. Boyce, A. Farhi, and R. Weishedel. Optimal Subset Selection. Springer-Verlag, 1974.

....always be the case. For example, if the data has redundant features but also has many missing values, a learning algorithm should induce a hypothesis which makes use of these redundant features. Thus the best feature subset is not always the minimal one. 5 RELATED WORK Researchers in statistics (Boyce, Farhi, Weischedel 1974; Narendra Fukunaga 1977; Draper Smith 1981; Miller 1990; Neter, Wasserman, Kutner 1990) and pattern recognition (Devijver Kittler 1982; Ben Bassat 1982) have investigated the feature subset selection problem for decades, but most work has concentrated on subset selection using linear ....

Boyce, D.; Farhi, A.; and Weischedel, R. 1974. Optimal Subset Selection. Springer-Verlag.

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D. Boyce, A. Farhi, and R Weischedel. Optimal Subset Selection. Springer-Verlag, Berlin, Germany, 1974. 44

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D. BOYCE, A. FARHI, AND R. WEISHEDEL. Optimal Subset Selection. Springer- Verlag, 1974.

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D. Boyce, A. Farhi, and R. Weishedel. Optimal Subset Selection. Springer-Verlag, 1974.

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