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A Feature Selection Based on Relevance and Redundancy
... The experiment data set is a part of sogou corpus of text classification full version [15]. We select nine categories from the corpus, namely automotive, finance, IT, health, sports, tourism, education, recruitment and ... See full document
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3D facial expression recognition using maximum relevance minimum redundancy geometrical features
... maximum relevance minimum redundancy (mRMR) model to aid in selecting the most relevant features in terms of class discrimina- tion and the most compact or non-redundant features to represent the face mesh ... See full document
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Hopfield Networks in Relevance and Redundancy Feature Selection Applied to Classification of Biomedical High-Resolution Micro-CT Images
... feature selection (e.g. [35]) or that use feature selection before feeding data into neural networks exist in manifold ...for feature selection in the minimal redundancy ... See full document
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Min-redundancy and max-relevance multi-view feature selection for predicting ovarian cancer survival using multi-omics data
... single-view feature selection algorithm to the multi-view ...multi-view feature selection method to predictive modeling from multi-omics ...multi-view feature selection ... See full document
13
Efficient Feature Selection via Analysis of Relevance and Redundancy
... search based on some correlation measure which evaluates the goodness of a subset by considering the individual predictive ability of each feature and the degree of correlation between ...forward ... See full document
20
Weighted Principle Component Analysis For Dimensionality Reduction In Medical Dataset
... process. Feature selection is an effective process to deal with high-dimensional ...The feature selection algorithm is efficient; it can explore labeled data and unlabeled data ...relevant ... See full document
6
A method of variables selection for soft sensor based on distributed mutual information
... The relevance and redundancy between the primary variable and auxiliary variables in the training data set were analyzed with the proposed method of distributed mutual ... See full document
6
An Improved Parallelized mRMR for Gene Subset Selection in Cancer Classification
... fields. Based on the previous researchers, the conventional approach for cancer classification is primarily based on the morphological appearance of the ...Minimum Redundancy Maximum Relevance ... See full document
6
Feature Selection with Ensembles, Artificial Variables, and Redundancy Elimination
... the relevance of the original variables with the relevance of random, artificial features (appended to the original data) constructed from the same distribution, but independently from the ...measure ... See full document
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An Approach to Fault Diagnosis of Rotating Machinery Using the Second Order Statistical Features of Thermal Images and Simplified Fuzzy ARTMAP
... minimum redundancy maximum relevance (mRMR) feature selection and simplified fuzzy ARTMAP (SFAM) classification is conducted for rotating machinery fault ...gorithm based on ... See full document
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Time–frequency based feature selection for discrimination of non-stationary biosignals
... lower feature space dimen- sionality, the 2D methodology allows to take into account the dynamic information of each spectral component over the t–f planes, which was reflected in more stable ...the ... See full document
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The Research of Reproducibility and Non redundancy Feature Selection Methods in Radiomics
... large feature sets compared to prior conventional radiologic analyses, it is expected that there may be redundancy in these features due to various limitations on the sample size, texture, and consistency ... See full document
8
Efficient Feature Selection by Using Global Redundancy Minimization and Constraint Score
... examination point in information mining, in light of the fact that the genuine information sets regularly have high dimensional elements, for example, the bioinformatics and content mining applications. Numerous current ... See full document
5
The Role of Feature Selection with Applications to Eye Movements using Electrooculography
... CBFS Feature selection algorithm using eye movements by ElectroOculoGraph (EOG) signals during reading and writing ...using feature selection and data mining classification ...(clearness ... See full document
8
INFORMATION TECHNOLOGY GOVERNANCE USING COBIT 4 0 DOMAIN DELIVERY SUPPORT AND MONITORING EVALUATION
... Constraint Selection-Based Semi-supervised Feature Selection ...in feature selection study when managing small-labeled among huge unlabeled data example from the similar ...new ... See full document
9
Comparative study of feature selection method of microarray data for gene classification
... of selection and classification techniques that has already been studied and developed to help in better classification of microarray ...gene selection and PNN ...gene selection since this technique ... See full document
27
Feature Selection for Computer-Aided Polyp Detection using MRMR
... lesions, selection of relevant features is of fundamental ...a feature selection scheme combining AdaBoost with the Minimum Redundancy Maximum Relevance (MRMR) to focus on the most ... See full document
9
Computational identification of surrogate genes for prostate cancer phases using machine learning and molecular network analysis
... phenotypes based on gene expres- sion microarray ...minimum redundancy – maximum relevance method (mRMR), a robust method with a broad spectrum of applications [13,17], to serve our goal of ... See full document
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A SURVEY ON RELEVANCE FEATURE SELECTION METHOD FOR TEXT CLASSIFICATION
... KNN is a case based learning algorithm. The objects are classified by selecting several labeled terms with their smallest distance from each object. The Major disadvantage of KNN is that it uses all features in ... See full document
5
Random forest based optimal feature selection for partial discharge pattern recognition in HV cables
... categories, feature selection is conducive to removal of the redundant and irrelevant features and to reduction of the computational complexity of the algorithm ...[8]. Feature selection ... See full document
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