Feature Selection: A Practitioner View
TLDR
A near comprehensive list of problems that have been solved using feature selection across technical and commercial domain is produced and can serve as a valuable tool to practitioners across industry and academia.Abstract:
Feature selection is one of the most important preprocessing steps in data mining and knowledge Engineering. In this short review paper, apart from a brief taxonomy of current feature selection methods, we review feature selection methods that are being used in practice. Subsequently we produce a near comprehensive list of problems that have been solved using feature selection across technical and commercial domain. This can serve as a valuable tool to practitioners across industry and academia. We also present empirical results of filter based methods on various datasets. The empirical study covers task of classification, regression, text classification and clustering respectively. We also compare filter based ranking methods using rank correlation.read more
Citations
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A hybrid intrusion detection system based on ABC-AFS algorithm for misuse and anomaly detection
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Sentiment Analysis of Review Datasets Using Naive Bayes and K-NN Classifier
TL;DR: The paper elaborately discusses two supervised machine learning algorithms: K-Nearest Neighbour(K-NN) and Naive Bayes and compares their overall accuracy, precisions as well as recall values and it was seen that in case of movie reviews Naïve Bayes gave far better results than K-NN but for hotel reviews these algorithms gave lesser, almost same accuracies.
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Optimal feature selection using a modified differential evolution algorithm and its effectiveness for prediction of heart disease
TL;DR: Various performance measures of integrating the modified differential evolution algorithm with fuzzy AHP and a feed-forward neural network in the prediction of heart disease are evaluated and the prediction time of the proposed hybrid model is evaluated and has shown promising results.
Journal ArticleDOI
Sentiment Analysis of Review Datasets Using Naïve Bayes' and K-NN Classifier
TL;DR: In this article, a sentiment focussed web crawling framework is proposed to facilitate the quick discovery of sentimental contents of movie reviews and hotel reviews and analysis of the same using statistical methods to capture elements of subjective style and sentence polarity.
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An integrated data-driven framework for urban energy use modeling (UEUM)
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References
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An introduction to variable and feature selection
Isabelle Guyon,André Elisseeff +1 more
TL;DR: The contributions of this special issue cover a wide range of aspects of variable selection: providing a better definition of the objective function, feature construction, feature ranking, multivariate feature selection, efficient search methods, and feature validity assessment methods.
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A review of feature selection techniques in bioinformatics
TL;DR: A basic taxonomy of feature selection techniques is provided, providing their use, variety and potential in a number of both common as well as upcoming bioinformatics applications.
Correlation-based Feature Selection for Machine Learning
TL;DR: This thesis addresses the problem of feature selection for machine learning through a correlation based approach with CFS (Correlation based Feature Selection), an algorithm that couples this evaluation formula with an appropriate correlation measure and a heuristic search strategy.