Month: December 2018

An accuracy comparison between scikit-learn’s SVM and LightTwinSVM program

Recently, I’ve introduced the LightTwinSVM program on my blog (If you haven’t read it, check out this post.).  It is a fast and simple implementation of TwinSVM classifier. Some people might ask why I should use this program over other popular SVM’s implementation such as LIBSVM and scikit-learn. The short answer is that TwinSVM has better accuracy than that of SVM in most cases.

In order to show the effectiveness of the LightTwinSVM program in terms of accuracy, experiments were conducted on 10 UCI datasets benchmark datasets.

Continue reading “An accuracy comparison between scikit-learn’s SVM and LightTwinSVM program”

LightTwinSVM: A Fast, Light-weight and Simple Implementation of TwinSVM Classifier

Support Vector Machine (SVM) is a popular and state-of-the-art classification algorithm.  Hence many packages and implementations of standard SVM can be found on the internet. However, there are some interesting extensions of SVM that has a slightly better prediction accuracy. Of these extensions, Twin Support Vector Machine (TSVM) has received more attention from scholars in the field of SVM research. Even I myself have published a classifier based on TSVM and KNN.

TSVM does classification using two non-parallel hyperplanes as opposed to a single hyperplane in standard SVM (To know more about TSVM, you can read this blog post.).  Unlike SVM, TSVM had almost no fast and simple implementation on the internet prior to 2018. So I decided to develop the LightTwinSVM program and share it with others for free.

Continue reading “LightTwinSVM: A Fast, Light-weight and Simple Implementation of TwinSVM Classifier”

A Brief Introduction to Twin Support Vector Machine Classifier

Support Vector Machine (SVM) was proposed by Vapnik and Cortes in 1995 [1]. It is a very popular and powerful classification algorithm. The main idea of SVM is to find an optimal separating hyperplane between two classes. Due to SVM’s great classification ability, it has been applied to a wide variety of applications.

Over the past decade, scholars have proposed classifiers on the basis of SVM. Among the extensions of SVM, I’d like to introduce Twin Support Vector Machine (TSVM) [2]. Because it has been received more attention.

Continue reading “A Brief Introduction to Twin Support Vector Machine Classifier”