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optimal feature selection

Cancer Classification using Self Adaptive Learning and  Optimal Feature Selection in SVM

Cancer Classification using Self Adaptive Learning and Optimal Feature Selection in SVM

... pattern selection process is integrated with the correlation coefficient based feature selection ...for optimal feature selection process is adapted to improve the SVM ...

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Random forest based optimal feature selection for partial discharge pattern recognition in HV cables

Random forest based optimal feature selection for partial discharge pattern recognition in HV cables

... SVM- and BPNN-based pattern recognition methods were applied to evaluate the validity of RF-based optimal feature selection according to pattern recognition accuracy. SVM has three key parameters: ...

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Optimal feature selection and machine learning for high level audio classification   a random forests approach

Optimal feature selection and machine learning for high level audio classification a random forests approach

... used feature spaces include Mel-frequency cepstral coefficients (MFCC), sub-band energy ratio, zero-crossing rates (ZCRs) ...as feature spaces; SVMs are adopted for general audio classification and GMMs for ...

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													Optimal feature selection algorithm for high  dimensional data sets using particle swarm optimization

1. Optimal feature selection algorithm for high dimensional data sets using particle swarm optimization

... to feature selection such as greedy based sequential forward selection (SFS) [2] and sequential backward selection (SBS) [3] ...varied feature selection approaches like ...

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A Combination Approach of Two Metaheuristic Algorithm for Optimal Feature Selection: Case Study Email Spam Detection

A Combination Approach of Two Metaheuristic Algorithm for Optimal Feature Selection: Case Study Email Spam Detection

... the selection of characteristics belongs to Oh et ...for feature selection, where DE was used to search for the optimal feature subset based on the solutions obtained by ...SA ...

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WEIGHTING INDIVIDUAL OPTIMAL FEATURE SELECTION IN NAIVE BAYES FOR TEXT CLASSIFICATION

WEIGHTING INDIVIDUAL OPTIMAL FEATURE SELECTION IN NAIVE BAYES FOR TEXT CLASSIFICATION

... This paper introduces support vector machine for text categorization. It provides both theoretical and empirical evidence that SVMS are very well suitable for text categorization the theoretical analysis concludes that ...

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Optimal Feature Selection by Genetic Algorithm for Classification Using Neural Network

Optimal Feature Selection by Genetic Algorithm for Classification Using Neural Network

... Genetic Algorithms are adaptive heuristic search algorithm premised on the evolutionary ideas of natural selection and genetic .In the past decades it had been widely used in various fields as an optimization ...

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Multilabelled Optimal Feature Classification Procedure for High Dimensional Bio Medical Data

Multilabelled Optimal Feature Classification Procedure for High Dimensional Bio Medical Data

... extract optimal feature selection with high dimensional bio-medical data, in this paper propose a Advance Machine Learning Approach with optimization approach ...

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Optimization Based Fuzzy Deep Learning Classification For Sentiment Analysis

Optimization Based Fuzzy Deep Learning Classification For Sentiment Analysis

... function selection and classification is proposed using optimized deep learning ...accuracy feature selection technique using convolution neutral network clubbed with evolutionary optimized ...the ...

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An Approach for Optimal Feature Subset Selection using a New Term Weighting Scheme and Mutual  Information

An Approach for Optimal Feature Subset Selection using a New Term Weighting Scheme and Mutual Information

... for feature selection which approximates Optimal Feature Selection model is presented in [4] and it is proved to have good efficiency and scalability which in some cases could lead to ...

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ABSRACT : Feature selection is a process which selects the subset of attributes from the original dataset by

ABSRACT : Feature selection is a process which selects the subset of attributes from the original dataset by

... unsupervised feature selection challenging and so it is hard to distinguish relevant features from the irrelevant ...some feature selection method that can be apply to supervised and ...

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Comparison of Feature Selection Strategies for Classification using Rapid Miner

Comparison of Feature Selection Strategies for Classification using Rapid Miner

... Optimize Selection (Evolutionary) : A genetic algorithm (GA) is a search heuristic that mimics the process of natural ...mutation, selection, and crossover. In genetic algorithm for feature ...

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Knowledgeable Handling of Impreciseness in Feature Subset Selection using Intuitionistic Fuzzy Mutual Information of Intrusion Detection System

Knowledgeable Handling of Impreciseness in Feature Subset Selection using Intuitionistic Fuzzy Mutual Information of Intrusion Detection System

... Saraet al [11] developed an IDS based feature selection model which integrates both filter and wrapper method. This work used linear correlation coefficient for feature grouping and cuttlefish ...

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A Novel Approach using Dual Active Feature Sample Selection and LTS (Learn to Search)

A Novel Approach using Dual Active Feature Sample Selection and LTS (Learn to Search)

... starting with single edge subgraph and their scores are calculated as in dual active feature sample selection. Then fastprobe followed by LTS algorithm is used to identify the 4 most discriminative ...

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Computational identification of deleterious synonymous variants in human genomes using a feature-based approach

Computational identification of deleterious synonymous variants in human genomes using a feature-based approach

... a feature-based computa- tional IDSV for identifying deleterious synonymous var- ...new feature based on the translation efficiency and function regions annotation traditional features based on splicing and ...

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Saturating Splines and Feature Selection

Saturating Splines and Feature Selection

... standardized feature in [0, 1]. The coordinate functions are shown for three values of τ , with the middle one corresponding to the value that minimizes cross-validation RMSE. When a coordinate function is zero, ...

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On the Stability of Feature Selection Algorithms

On the Stability of Feature Selection Algorithms

... domain-specific feature selection methods from which to pick, surveyed in several previous works, ...each feature selection method is, with respect to small changes in the training ...the ...

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Quadratic Programming Feature Selection

Quadratic Programming Feature Selection

... The central idea of the MaxDep approach is to find a subset of features which jointly have the largest dependency on the target class. However, it is often infeasible to compute the joint density functions of all ...

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Human-in-the-Loop Feature Selection

Human-in-the-Loop Feature Selection

... per-example feature selection method by including the ...each feature and derive a policy that produces a new feature subset for each observa- tion in the ...The feature ...

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Feature Selection for Unsupervised Learning

Feature Selection for Unsupervised Learning

... a feature dependence measure to select ...the feature subset and finding the optimal number of clusters for a document clustering problem using a Bayesian statistical estimation ...one feature ...

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