Feature subsets
Feature Selection with the R Package MXM: Discovering Statistically Equivalent Feature Subsets
... multiple feature subsets are approximately equally predictive for a given ...equivalent feature subsets has several advantages in our ...equally-predictive subsets actually exist ...
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Different Feature Selection of Soil Attributes Influenced Clustering Performance on Soil Datasets
... different feature subsets of soil data ...features subsets. The feature subsets containing envi- ronmental attributes helped to improve clustering performances better than those having ...
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An Efficient Decision Tree Model for Classification of Attacks with Feature Selection
... based feature selection technique is used to select critical features and to reduce unimportant features to be applied to deduct random forest model, which is obtained as one of the best ...reduced feature ...
7
Reducing Weight Undertraining in Structured Discriminative Learning
... different subsets of data, rather than dif- ferent ...a feature subset on which to grow each tree using standard decision tree ...random feature at each ...
7
Using Gazetteers in Discriminative Information Extraction
... In work developed independently and in parallel to the work presented here, Sutton et al. (2006) iden- tify general problems with gazetteer features and propose a solution similar to ours. They present re- sults on ...
8
Aggregating case based reasoners in ensembles : an approach in support of explanation
... issue. Feature Selection can be seen as a “training” process, in the sense that we need to explore a whole set o f feature combinations to decide which one best represents the ...a feature selection ...
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Combined Use of k Mer Numerical Features and Position Specific Categorical Features in Fixed Length DNA Sequence Classification
... the feature ranking to conduct feature selection. The used feature selection technique is a greedy based on technique which does not learning and predicting on all possible feature ...
12
Creating diverse nearest neighbour ensembles using simultaneous metaheuristic feature selection
... chosen feature subsets (the RSS method [31]), and ee have published results elsewhere [46] showing that feature selection can bring improvements for single classifiers of various different types that ...
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Overfitting and Diversity in Classification Ensembles based on Feature Selection
... possible subsets of this p feature set and each subset can be represented as a feature mask of 1s and ...different feature subsets can easily be produced using a random number ...
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Overfitting in Wrapper Based Feature Subset Selection: The Harder You Try the Worse it Gets
... in feature selection appears to be exacerbated by the intensity of the search since the more feature subsets that are visited the more likely the search is to find a subset that overfits ...the ...
11
Applying data mining techniques to improve diagnosis in neonatal jaundice
... all feature subsets, we found that, when applied to the subset containing risk factors and transcutaneous bilirubin levels, the variables with higher influence are, in descending order: TcB in the range ...
6
Machine learning classifiers provide insight into the relationship between microbial communities and bacterial vaginosis
... the feature importance ...the feature and ...five-feature subsets and the mean coefficient magnitude across validation datasets divided by the standard ...five-feature subsets ...
9
Case Representation Issues for Case Based Reasoning from Ensemble Research
... of feature selection to create an ensemble of classifiers; among them those ones made by Cherkauer (1995), Ho (1998a, 1998b), Guerra-Salcedo and Whitney (1999a, 1999b) Tumer and Ghosh (1996) and Cunningham and ...
12
Africans, Cherokees, and the ABCFM Missionaries in the Nineteenth Century: An Unusual Story of Redemption
... traditional feature selection methods are used to form the feature pool in our framework, that is, SVM-RFE [102$], T-statistics, and an entropy-based feature selection method ...evaluate ...
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Identifying Protein Features Responsible for Improved Drug Repurposing Accuracies Using The CANDO Platform: Implications for Drug Design
... Figure 2. Superset, subpar set, random set, and PCA matrices average indication accuracies sorted by size. Average indication accuracies are shown for the supersets (blue) generated using the best subsets ranked ...
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Activation of Invariant Natural Killer T Cell Subsets in C57BL/6J Mice by Different Injection Modes of α-galactosylceramide
... As a classic iNKT cell-specific activator, α-GalCer is a galactosylceramide found in marine sponge extracts, which stimulates CD1d-restricted iNKT cell activation, 4,15 effectively activating iNKT1, iNKT2, and iNKT17. ...
10
Transitive subsets for semigroup actions
... Remark 2.4. X is a transitive subset of (S, X ) if and only if (S, X) is topologically transitive. Definition 2.5. Let (S, X) be a dynamical system and A be a nonempty closed subset of X but not a singleton. A is called ...
9
A Review on Filter Based Feature Selection
... the feature selection is an integral component for the effective use of data mining tools and techniques ...A feature refers to an aspect of the data. Feature Selection (FS) is a method of selecting ...
11
A NOVEL HYBRID METHOD FOR GENE SELECTION IN MICROARRAY BASED CANCER CLASSIFICATION
... ANOVA is a frequently used technique for finding the differentially expressed genes in the analysis of microarray data. Based on the p-values, the informative gene subsets are selected as demonstrated by Soliman ...
7
Learning with Similarities on Subsets
... It has been observed that learning features using certain unsupervised learning al- gorithms can lead to better performance on certain supervised learning tasks and one explanation that has been proposed for why this is ...
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