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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Journal ArticleDOI
Feature subset selection using differential evolution and a wheel based search strategy
TL;DR: A new feature selection method that utilizes differential evolution in a novel manner to identify relevant feature subsets using a simple, yet powerful, procedure that involves distributing the features among a set of wheels.
Journal ArticleDOI
Artificial neural network for Cu quantitative determination in soil using a portable Laser Induced Breakdown Spectroscopy system
Edilene Cristina Ferreira,Débora Marcondes Bastos Pereira Milori,Ednaldo José Ferreira,Robson Marinho da Silva,Robson Marinho da Silva,Ladislau Martin-Neto +5 more
TL;DR: The proposed method presented a limit of detection of 2.3 mg dm− 3 of Cu and a mean squared error (MSE) of 0.5 for the predictions, and showed good efficiency for Cu predictions although the features of portable instrumentation employed.
Journal ArticleDOI
A hybrid evolutionary algorithm for attribute selection in data mining
TL;DR: Improvements are made to the GA-SVM hybrid by using a correlation measure between attributes as a fitness measure to replace the weaker members in the population with newly formed chromosomes and injects greater diversity and increases the overall fitness of the population.
Compression Artifacts Removal Using Convolutional Neural Networks
TL;DR: This paper shows that it is possible to train large and deep convolutional neural networks for JPEG compression artifacts reduction, and that such networks can provide significantly better reconstruction quality compared to previously used smaller networks as well as to any other state-of-the-art methods.
Journal ArticleDOI
Non-Negative Spectral Learning and Sparse Regression-Based Dual-Graph Regularized Feature Selection
TL;DR: The objective function, the iterative updating rules and a proof of convergence are explained, showing that NSSRD is significantly more effective than several other feature selection algorithms from the literature, on a variety of test data.
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.
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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.