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
Simultaneous Feature Selection and Support Vector Machine Optimization Using the Grasshopper Optimization Algorithm
Ibrahim Aljarah,Ala' M. Al-Zoubi,Hossam Faris,Mohammad A. Hassonah,Seyedali Mirjalili,Heba Saadeh +5 more
TL;DR: A hybrid approach based on the Grasshopper optimisation algorithm (GOA), which is a recent algorithm inspired by the biological behavior shown in swarms of grasshoppers, is proposed to optimize the parameters of the SVM model, and locate the best features subset simultaneously.
Journal ArticleDOI
Assessment of Tremor Activity in the Parkinson’s Disease Using a Set of Wearable Sensors
George Rigas,Alexandros T. Tzallas,Markos G. Tsipouras,Panagiota Bougia,Evanthia E. Tripoliti,Dina Baga,Dimitrios I. Fotiadis,Sofia Tsouli,Spiros Konitsiotis +8 more
TL;DR: An automated method for both resting and action/postural tremor assessment is proposed using a set of accelerometers mounted on different patient's body segments that quantifies tremor severity with 87 % accuracy and discriminates tremor from other Parkinsonian motor symptoms during daily activities.
Book ChapterDOI
Preventing Student Dropout in Distance Learning Using Machine Learning Techniques
TL;DR: A number of experiments have taken place with data provided by the ‘informatics’ course of the Hellenic Open University and a quite interesting conclusion is that the Naive Bayes algorithm can be successfully used.
Journal ArticleDOI
Detection of early plant stress responses in hyperspectral images
TL;DR: An approach which combines unsupervised and supervised methods in order to identify several stages of progressive stress development from series of hyperspectral images, and it is shown that some VIs have overall relevance, while others are specific to particular senescence stages.
Journal ArticleDOI
Local Causal and Markov Blanket Induction for Causal Discovery and Feature Selection for Classification Part II: Analysis and Extensions
Constantin F. Aliferis,Alexander Statnikov,Ioannis Tsamardinos,Subramani Mani,Xenofon Koutsoukos +4 more
TL;DR: The empirical convergence of GLL to the true local neighborhood as a function of sample size is investigated and the role of the algorithm parameters is discussed and it is shown that Markov blanket and causal graph concepts can be used to understand deviations from optimality of state-of-the-art non-causal algorithms.
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.