[PDF][PDF] Feature selection for machine learning classification problems: a recent overview
S Kotsiantis - Artificial Intelligence Review, 2011 - cs.upc.edu
A lot of candidate features are usually provided to a learning algorithm for producing a
complete characterization of the classification task. However, it is often the case that majority …
complete characterization of the classification task. However, it is often the case that majority …
[HTML][HTML] A framework for feature selection through boosting
As dimensions of datasets in predictive modelling continue to grow, feature selection
becomes increasingly practical. Datasets with complex feature interactions and high levels …
becomes increasingly practical. Datasets with complex feature interactions and high levels …
Feature evaluation and selection with cooperative game theory
Recent years, various information theoretic based measurements have been proposed to
remove redundant features from high-dimensional data set as many as possible. However …
remove redundant features from high-dimensional data set as many as possible. However …
Feature-selected tree-based classification
Feature selection can decrease classifier size and improve accuracy by removing noisy
and/or redundant features. However, it is possible for feature selection to yield features that …
and/or redundant features. However, it is possible for feature selection to yield features that …
Accurate prediction of coronary artery disease using reliable diagnosis system
I Mandal, N Sairam - Journal of medical systems, 2012 - Springer
This paper presents more accurate and reliable computational methods for aiding the
treatment of people with coronary artery disease. New techniques are introduced for …
treatment of people with coronary artery disease. New techniques are introduced for …
A general framework for boosting feature subset selection algorithms
J Pérez-Rodríguez, A de Haro-Garcia, JAR del Castillo… - Information …, 2018 - Elsevier
Feature selection is one of the most important tasks in many machine learning and data
mining problems. Due to the increasing size of the problems, removing useless, erroneous …
mining problems. Due to the increasing size of the problems, removing useless, erroneous …
Beyond biometrics
EL Van Den Broek - Procedia Computer Science, 2010 - Elsevier
Throughout the last 40 years, the essence of automated identification of users has remained
the same. In this article, a new class of biometrics is proposed that is founded on processing …
the same. In this article, a new class of biometrics is proposed that is founded on processing …
Glypre: in silico prediction of protein glycation sites by fusing multiple features and support vector machine
Glycation is a non-enzymatic process occurring inside or outside the host body by attaching
a sugar molecule to a protein or lipid molecule. It is an important form of post-translational …
a sugar molecule to a protein or lipid molecule. It is an important form of post-translational …
Weighted Combination of Łukasiewicz implication and Fuzzy Jaccard similarity in Hybrid Ensemble Framework (WCLFJHEF) for Gene Selection
A framework is developed for gene expression analysis by introducing fuzzy Jaccard
similarity (FJS) and combining Łukasiewicz implication with it through weights in hybrid …
similarity (FJS) and combining Łukasiewicz implication with it through weights in hybrid …
A genetic algorithm based feature selection for handwritten digit recognition
Background: The data proliferation has been resulted in large-scale, high dimensional data
and brings new challenges for feature selection in handwriting recognition problems. The …
and brings new challenges for feature selection in handwriting recognition problems. The …