Linear Penalization Support Vector Machines for Feature Selection

Anomaly Detection Using SVM as Classifier and Decision Tree for Optimizing Feature Vectors

Abstract- With the advancement and development of computer network technologies, the way for intruders has become smoother; therefore, to detect threats and attacks, the importance of intrusion detection systems (IDS) as one of the key elements of security is increasing. One of the challenges of intrusion detection systems is managing of the large amount of network traffic features. Removing un...

Modeling and design of a diagnostic and screening algorithm based on hybrid feature selection-enabled linear support vector machine classification

Background: In the current study, a hybrid feature selection approach involving filter and wrapper methods is applied to some bioscience databases with various records, attributes and classes; hence, this strategy enjoys the advantages of both methods such as fast execution, generality, and accuracy. The purpose is diagnosing of the disease status and estimating of the patient survival. Method...

Feature Selection Using Multi Objective Genetic Algorithm with Support Vector Machine

Different approaches have been proposed for feature selection to obtain suitable features subset among all features. These methods search feature space for feature subsets which satisfies some criteria or optimizes several objective functions. The objective functions are divided into two main groups: filter and wrapper methods.  In filter methods, features subsets are selected due to some measu...

Feature Selection Using Linear Support Vector Machines

Kernel/feature Selection for Support Vector Machines

Support Vector Machines are classifiers with architectures determined by kernel functions. In these proceedings we propose a method for selecting the best SVM kernel for a given classification problem. Our method searches for the best kernel by remapping the data via a kernel variant of the classical Gram-Schmidt orthonormalization procedure then using Fisher’s linear discriminant on the remapp...