Correlation-based Feature Selection for Machine Learning

By Mark A. Hall. 2003

4,122 citations recorded by the source corpus.

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Citations in the corpus

A practical tutorial on autoencoders for nonlinear feature fusion: Taxonomy, models, software and guidelines (2018, methodology): “rried out using computer algorithms. These can operate in supervised or unsupervised manner. The former approach usually relies on correlation or mutual information between input and output variables [24, 25], while the latter tends to avoid redundancy among features [26]. Feature selection is overa”
A comparative study on feature selection and classification methods using gene expression profiles and proteomic patterns. (2002, unknown): “Symmetrical uncertainties are used in CFS to estimate the degree of association between discrete features or between features and classes [9].”
Filter Methods for Feature Selection - A Comparative Study (2007, unknown): “6 (??) 100 200 150 4,6,8,11,17,19 [1-11],[13-20],38,44,100, 112,120,123,144,172,180,185,191,197,200”
Classification and Prediction of Brain Disorders Using Functional Connectivity: Promising but Challenging (2018, unknown): “Correlation-based feature selection (CFS) is a simple algorithm which ranks features based on a hypothesis that good feature subsets contain features highly correlated with the classification (Hall, 1999; Shen et al., 2010; Tang et al., 2012; Su et al., 2013; Challis et al., 2015).”
TSE-IDS: A Two-Stage Classifier Ensemble for Intelligent Anomaly-Based Intrusion Detection System (2019, methodology): “In this work, we choose a correlationbased feature selection, which estimates the importance of features using entropy and information gain [55].”
Recognising realistic emotions and affect in speech: State of the art and lessons learnt from the first challenge (2011, background): “Apart from wrappers, less computationally expensive ‘ﬁlter’ or ‘open loop’ methods have attracted interest, such as information theoretic ﬁlters and correlation-based analysis (Hall, 1998).”
Correlation feature selection based improved-Binary Particle Swarm Optimization for gene selection and cancer classification (2018, methodology): “The Naive–Bayes classifier(NB) is used to valuate the predictive ability of the optimal subset of genes and, tratified 10-fold cross-validation is performed to reduce the risk f overfitting.”
A review of feature extraction and performance evaluation in epileptic seizure detection using EEG (2019, methodology): “Fast correlation based-filter proposed in [YL04], correlation-based selection (CFS) [Hal99], and ReliefF established in [KŠRŠ97], were utilized to select non-redundant features and the filtered features were classified by an artificial neural network (ANN).”
Knowledge-based Systems (2009, unknown): “Specifically, this algorithm takes into ccount the redundancy of the features in a subset, and their ventual correlation with the class itself [40].”
Global Model Interpretation Via Recursive Partitioning (2018, methodology): “e feature selection process could either be conducted before the model ing [11] or embedded into it [24, 27].”
AVEC 2013: the continuous audio/visual emotion and depression recognition challenge (2013, methodology): “The baseline method uses correlation based feature selection (CFS) [12] and -Support Vector Machine Regressors ( -SVR) with an intersection kernel.”
DATA MINING TECHNIQUES (2003, methodology): “A well-known method in this category is the CFS method [34].”
Radiomics with artificial intelligence: a practical guide for beginners. (2019, methodology): “rithm (59), ReliefF (66), and Gini index (67).”