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Showing 1 to 9 of 9 for “"Feature Ranking"”.
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In silico prediction of non-coding RNAs using supervised learning and feature ranking methods
… RNA (ncRNA) classification model based on features derived from folding the consensus sequence of multiple sequence alignments using different folding programs: RNAalifold, CentroidFold, and RSpredict. The method ranks these folding features according to a Class Separation Measure (CSM) …
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Ensemble of Feature Selection Techniques for High Dimensional Data
… warehouses, or other information repositories. Feature selection is an important preprocessing step of data mining that helps increase the predictive performance of a model. The main aim of feature selection is to choose a subset of features with high predictive information and eliminate …
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Performance of Malware Classification on Machine Learning using Feature Selection
… to their family the research provides four feature selection algorithms to select best feature for multiclass classification problem. Comparing. Then find these algorithms top 100 features are selected to performance evaluations. Five machine learning algorithms is compared to find best …
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Design and implementation of a cyberinfrastructure for RNA motif search, prediction and analysis
… algorithm, a pseudoknot removal algorithm, and a feature ranking algorithm based on the gini impurity measure. A series of experiments including 10-fold cross- validation has been conducted to evaluate the performance of the Junction-Explorer tool. Experimental results demonstrate the …
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FORMULATION OF DETECTION STRATEGIES IN IMAGES
… and SVS images prior to fusion. The most notable feature of the registration procedure is that it is guided by the information extracted from the weather-invariant SVS images. Four fusion rules based on combining Discrete Wavelet Transform (DWT) sub-bands are implemented and evaluated. The …
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Classification Techniques Using EHG Signals for Detecting Preterm Births
… and 38 who delivered prematurely. Several new features from Electromyography studies are utilized, as well as feature-ranking techniques to determine their discriminative capabilities in detecting term and preterm records. The results illustrate that the combination of the Levenberg-Marquardt …
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Dimensionality Reduction and Fusion Strategies for the Design of Parametric Signal Classifiers
… information. The criteria considered for ranking transform coefficients include magnitude, variance, inter-class separation, and classification accuracies of individual transform coefficients. The ranking strategy not only facilitates overcoming the dimensionality curse for multivariate …
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Ranking features used in modeling student collaboration using multimodal learning analytics
Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2027-08-01
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Hybrid Methods for Feature Selection
<p>Feature selection is one of the important data preprocessing steps in data mining. The feature selection problem involves finding a feature subset such that a classification model built only with this subset would have better predictive accuracy than model built with a complete set of features. …