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
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Patent
Managing electronic messages
TL;DR: In this paper, a sender computer system may send one, two, or more challenge messages to the recipient of the electronic messages in determining whether to deliver the electronic message, based on the response or lack of response to the challenge messages.
Journal ArticleDOI
Deep learning encodes robust discriminative neuroimaging representations to outperform standard machine learning
Anees Abrol,Zening Fu,Mustafa Salman,Rogers F. Silva,Yuhui Du,Yuhui Du,Sergey M. Plis,Vince D. Calhoun +7 more
TL;DR: In this article, the authors conduct a large-scale systematic comparison profiled in multiple classification and regression tasks on structural MRI images and show the importance of representation learning for deep learning for brain imaging data analysis.
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Efficient semi-supervised feature selection with noise insensitive trace ratio criterion
TL;DR: This paper proposes a noise insensitive trace ratio criterion for feature selection with a re-scale preprocessing and proposes an efficient semi-supervised feature selection algorithm to select relevant features using both labeled and unlabeled data.
Journal ArticleDOI
Classification model selection via bilevel programming
TL;DR: This work proposes a bilevel program that is significantly more versatile than commonly used grid search procedures, enabling the use of models with many hyper-parameters, and demonstrates the practicality of this approach for model selection in machine learning.
Journal ArticleDOI
Evolution of Plastic Learning in Spiking Networks via Memristive Connections
TL;DR: A spiking neuroevolutionary system which implements memristors as plastic connections, i.e., whose weights can vary during a trial, which provides an in-depth analysis of network structure and demonstrates that memristive plasticity enables higher performance than constant-weighted connections in both static and dynamic reward scenarios.
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.