Wrappers for feature subset selection
Ron Kohavi,George H. John +1 more
TLDR
The wrapper method searches for an optimal feature subset tailored to a particular algorithm and a domain and compares the wrapper approach to induction without feature subset selection and to Relief, a filter approach tofeature subset selection.About:
This article is published in Artificial Intelligence.The article was published on 1997-12-01 and is currently open access. It has received 8610 citations till now. The article focuses on the topics: Feature selection & Minimum redundancy feature selection.read more
Citations
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Recursive cluster elimination (RCE) for classification and feature selection from gene expression data
TL;DR: The success of the SVM-RCE method in classification suggests that gene interaction networks or other biologically relevant metrics that group genes based on functional parameters might also be useful.
Journal ArticleDOI
Machine learning identification of EEG features predicting working memory performance in schizophrenia and healthy adults
TL;DR: EEG features derived by SVM are consistent with literature reports of gamma’s role in memory encoding, engagement of theta during memory retention, and elevated resting low-frequency activity in schizophrenia.
Journal ArticleDOI
A hybrid wavelet-ELM based short term price forecasting for electricity markets
TL;DR: This study investigates the performance of a novel neural network technique called Extreme Learning Machine (ELM) in the price forecasting problem and demonstrates that the proposed method is one of the most suitable price forecasting techniques.
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Estimating standing biomass in papyrus Cyperus papyrus L. swamp: exploratory of in situ hyperspectral indices and random forest regression
TL;DR: In this paper, the utility of random forest RF regression and two narrow-band vegetation indices in estimating above-ground biomass AGB for complex and densely vegetated swamp canopies was evaluated.
Prediction of Wind Farm Power Ramp Rates: A Data-Mining
Haiyang Zheng,Andrew Kusiak +1 more
TL;DR: In this paper, multivariate time series models were built to predict the power ramp rates of a wind farm using data-mining algorithms and the support vector machine regression algorithm performed best out of the five algorithms studied.
References
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Book
Genetic algorithms in search, optimization, and machine learning
TL;DR: In this article, the authors present the computer techniques, mathematical tools, and research results that will enable both students and practitioners to apply genetic algorithms to problems in many fields, including computer programming and mathematics.
Journal ArticleDOI
Classification and Regression Trees.
Book
C4.5: Programs for Machine Learning
TL;DR: A complete guide to the C4.5 system as implemented in C for the UNIX environment, which starts from simple core learning methods and shows how they can be elaborated and extended to deal with typical problems such as missing data and over hitting.
Book
Applied Regression Analysis
Norman R. Draper,Harry Smith +1 more
TL;DR: In this article, the Straight Line Case is used to fit a straight line by least squares, and the Durbin-Watson Test is used for checking the straight line fit.
Journal ArticleDOI
Induction of Decision Trees
TL;DR: In this paper, an approach to synthesizing decision trees that has been used in a variety of systems, and it describes one such system, ID3, in detail, is described, and a reported shortcoming of the basic algorithm is discussed.