scholarly journals RETRACTED ARTICLE: Feature selection for machine learning classification problems: a recent overview

2011 ◽  
Vol 42 (1) ◽  
pp. 157-157 ◽  
Author(s):  
S. B. Kotsiantis
Keyword(s):  
Machine Learning ◽  
Feature Selection ◽  
Classification Problems ◽  
Machine Learning Classification ◽  
Retracted Article ◽  
Selection For

scholarly journals Machine learning methods in the computational biology of cancer

2014 ◽  
Vol 470 (2167) ◽  
pp. 20140081 ◽  
Author(s):  
M. Vidyasagar
Keyword(s):  
Machine Learning ◽  
Ovarian Cancer ◽  
Feature Selection ◽  
Classification Problems ◽  
Open Problems ◽  
Machine Learning Methods ◽  
Selection For ◽  
Personalized Cancer Therapy ◽  
Personalized Cancer ◽  
Sparse Feature Selection

The objectives of this Perspective paper are to review some recent advances in sparse feature selection for regression and classification, as well as compressed sensing, and to discuss how these might be used to develop tools to advance personalized cancer therapy. As an illustration of the possibilities, a new algorithm for sparse regression is presented and is applied to predict the time to tumour recurrence in ovarian cancer. A new algorithm for sparse feature selection in classification problems is presented, and its validation in endometrial cancer is briefly discussed. Some open problems are also presented.

Feature Selection for Unsupervised Machine Learning of Accelerometer Data Physical Activity Clusters – A Systematic Review

2021 ◽  
Author(s):  
Petra J. Jones ◽  
Mike Catt ◽  
Melanie J. Davies ◽  
Charlotte L. Edwardson ◽  
Evgeny M. Mirkes ◽  
...  
Keyword(s):  
Physical Activity ◽  
Machine Learning ◽  
Systematic Review ◽  
Feature Selection ◽  
Accelerometer Data ◽  
Unsupervised Machine Learning ◽  
Selection For

Evolution-In-Materio: Solving Machine Learning Classification Problems Using Materials

2014 ◽  
pp. 721-730 ◽  
Author(s):  
Maktuba Mohid ◽  
Julian Francis Miller ◽  
Simon L. Harding ◽  
Gunnar Tufte ◽  
Odd Rune Lykkebø ◽  
...  
Keyword(s):  
Machine Learning ◽  
Classification Problems ◽  
Machine Learning Classification

scholarly journals Application of all relevant feature selection for failure analysis of parameter-induced simulation crashes in climate models

2015 ◽  
Vol 8 (7) ◽  
pp. 5419-5435 ◽  
Author(s):  
W. Paja ◽  
M. Wrzesień ◽  
R. Niemiec ◽  
W. R. Rudnicki
Keyword(s):  
Machine Learning ◽  
Feature Selection ◽  
Climate Models ◽  
Original Study ◽  
Relative Importance ◽  
Relevant Feature ◽  
Machine Learning Methods ◽  
Selection For ◽  
Robust Prediction ◽  
Physical Components

Abstract. The climate models are extremely complex pieces of software. They reflect best knowledge on physical components of the climate, nevertheless, they contain several parameters, which are too weakly constrained by observations, and can potentially lead to a crash of simulation. Recently a study by Lucas et al. (2013) has shown that machine learning methods can be used for predicting which combinations of parameters can lead to crash of simulation, and hence which processes described by these parameters need refined analyses. In the current study we reanalyse the dataset used in this research using different methodology. We confirm the main conclusion of the original study concerning suitability of machine learning for prediction of crashes. We show, that only three of the eight parameters indicated in the original study as relevant for prediction of the crash are indeed strongly relevant, three other are relevant but redundant, and two are not relevant at all. We also show that the variance due to split of data between training and validation sets has large influence both on accuracy of predictions and relative importance of variables, hence only cross-validated approach can deliver robust prediction of performance and relevance of variables.

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