Add to ORKGAbstract: Most of the commonly known feature selection methods focus on selecting ap-propriate predictors for image recognition or generally on data mining issues. In this paper we present a comparison between widely used Recursive Feature Elimination (RFE) with resampling method and the Relaxed Linear Separability (RLS) approach with application to the analysis of the data sets resulting from gene expression experiments. Different types of classification algorithms such as K-Nearest Neighbours (KNN), Support Vector Machines (SVM) and Random Forests (RF) are exploited and compared in terms of classification ac-curacy with optimal set of genes treated as predictors selected by either the RFE or the RLS approaches. Ten-fold cross-validation w...
Abstract: In fact, cancer is produced for genetic reasons. So, gene feature selection techniques are...
For many functional genomic experiments, identifying the most characterizing genes is a main challen...
Background Several classification and feature selection methods have been studied for the identifica...
Most of the commonly known feature selection methods focus on selecting appropriate predictors for i...
2 One advantage of the microarray technique is that it allows scientists to explore the ex-pression ...
AbstractClassification of gene expression data plays a significant role in prediction and diagnosis ...
Gene expression profiles obtained by high-throughput techniques such as microarray provide a snapsho...
Microarray expression studies are producing massive high-throughput quantities of gene expression an...
We examine feature selection algorithms for handling data sets with many features. We introduce the ...
The application of gene expression data to the diagnosis and classification of cancer has become a h...
Feature selection and classification are the main topics in microarray data analysis. Although many ...
The paper presents the fusion approach of different feature selection methods in pattern recognition...
The paper presents the fusion approach of different feature selection methods in pattern recognition...
The paper presents the fusion approach of different feature selection methods in pattern recognition...
The high performance implementations of machine learning algorithms have been enhanced by recent dev...
Abstract: In fact, cancer is produced for genetic reasons. So, gene feature selection techniques are...
For many functional genomic experiments, identifying the most characterizing genes is a main challen...
Background Several classification and feature selection methods have been studied for the identifica...
Most of the commonly known feature selection methods focus on selecting appropriate predictors for i...
2 One advantage of the microarray technique is that it allows scientists to explore the ex-pression ...
AbstractClassification of gene expression data plays a significant role in prediction and diagnosis ...
Gene expression profiles obtained by high-throughput techniques such as microarray provide a snapsho...
Microarray expression studies are producing massive high-throughput quantities of gene expression an...
We examine feature selection algorithms for handling data sets with many features. We introduce the ...
The application of gene expression data to the diagnosis and classification of cancer has become a h...
Feature selection and classification are the main topics in microarray data analysis. Although many ...
The paper presents the fusion approach of different feature selection methods in pattern recognition...
The paper presents the fusion approach of different feature selection methods in pattern recognition...
The paper presents the fusion approach of different feature selection methods in pattern recognition...
The high performance implementations of machine learning algorithms have been enhanced by recent dev...
Abstract: In fact, cancer is produced for genetic reasons. So, gene feature selection techniques are...
For many functional genomic experiments, identifying the most characterizing genes is a main challen...
Background Several classification and feature selection methods have been studied for the identifica...