Earticle

This paper proposes a Multiclass Least Squares Twin Support Vector Machine (MLSTSVM) classifier for multi-class classification problems. The formulation of MLSTSVM is obtained by extending the formulation of recently proposed binary Least Squares Twin Support Vector Machine (LSTSVM) classifier. For M-class classification problem, the proposed classifier seeks M-non parallel hyper-planes, one for each class, by solving M-linear equations. A regularization term is also added to improve the generalization ability. MLSTSVM works well for both linear and non-linear type of datasets. It is relatively simple and fast algorithm as compared to the other existing approaches. The performance of proposed approach has been evaluated on twelve benchmark datasets. The experimental result demonstrates the validity of proposed MLSTSVM classifier as compared to the typical multi-classifiers based on ‘Support Vector Machine’ and ‘Twin Support Vector Machine’. Statistical analysis of the proposed classifier with existing classifiers is also performed by using Friedman’s Test statistic and Nemenyi post hoc techniques.

This research work proposes a Weighted Least Square Twin Support Vector Machine (WLSTSVM) for imbalanced dataset. Real world data are imbalanced in nature due to which most of the classification techniques do not work well. In Imbalanced data, there is a huge difference between the numbers of data samples of classes. One class data samples are larger as compared to other class data samples. This paper discusses the traditional methods of handling imbalanced data and proposes an improvement over Least Square Twin Support Vector Machine. This research work has performed experiment on five benchmark UCI datasets using 10-fold cross validation method. The results of experiment show that the proposed technique performed well for imbalanced dataset and its accuracy is better as compared to other existing methods. This research work presents the formulation of proposed approach for both linear and non-linear data samples.

Twin support vector machine (TWSVM) was initially designed for binary classification. However, real-world problems often require the discrimination more than two categories. To tackle multi-class classification problem, in this paper, a multiple least squares twin support vector machine is proposed. Our Multi-LSTSVM solves K quadratic programming problems (QPPs) to obtain K hyperplanes, each problem is similar to binary LSTSVM. Comparison against the Multi-LSSVM, Multi-GEPSVM, Multi-TWSVM and our Multi-LSTSVM on both UCI datasets and ORL, YALE face datasets illustrate the effectiveness of the proposed method.

It is evident from various researches that disease diagnosis using machine learning methods has been increasing rapidly. In this research work, feature selection based Least Square Twin Support Vector Machine (LSTSVM), which is a machine learning method, is used for diagnosis of heart diseases. In this approach F-score is used to calculate the weight of each feature and then features are selected according to their weight. The higher weight is assigned to the feature having high F-score. Grid search approach is also utilized to select the best value of classifier's parameters in order to enhance its performance. The heart-statlog disease dataset is used in this study, which is taken from the UCI repository. The performance of proposed model with different feature sets has been evaluated for different training-test datasets. The results indicate that LSTSVM model with 11 features has achieved highest accuracy. The results are very promising as compared to the other approaches proposed earlier.

Support Vector Machine(SVM)은 통계적 학습 이론에 기반을 둔 분류기이다. 또한 Twin Support Vector Machine(TWSVM)은 이진 SVM 분류기의 한 종류로써, 서로 관련된 두 개의 SVM 유형 문제를 통해 평행하지 않은 두개의 평면을 결정하고 이 두 평면을 통해 분류기를 완성하는 방식이다. 이러한 방식의 TWSVM은 학습 시간이 SVM에 비해 훨씬 짧으며, SVM과 비교하여 떨어지지 않는 성능을 보여준다. 본 논문은 분류기 입력에 Fuzzy Membership을 적용하는 방식의 TWSVM을 제안하고, 전리층 레이더 데이터를 이용한 실험을 통하여 기존에 세시 되었던 분류기와 비교한다.

Support Vector machine is the classifier which is based on the statistical training theory. Twin Support Vector Machine(TWSVM) is a kind of binary classifier that determines two nonparallel planes by solving two related SVM-type problems. The training time of TWSVM is shorter than that of SVM, but TWSVM doesn't shows worse performance than that of SVM. This paper proposes the TWSVM which is applied fuzzy membership, and compares the performance of this classifier with the other classifiers using Ionosphere radar data set.

Support Vector Machine(SVM)은 통계적 학습 이론에 기반을 둔 분류기이다. 또한 Twin Support Vector Machine(TWSVM)은 이진 SVM 분류기의 한 종류로써, 서로 관련된 두 개의 SVM 유형 문제를 통해 평행하지 않은 두 개의 평면을 결정하고 이 두 평면을 통해 분류기를 완성하는 방식이다. 이러한 방식은 TWSVM은 학습 시간이 SVM에 비해 훨씬 짧으며, SVM과 비교하여 떨어지지 않는 성능을 보여준다. 본 논문은 분류기 입력에 Fuzzy Memvership을 적용하는 방식의 TWSVM을 제안하고, 2차원 벡터 입력에 대한 실험을 통하여 기존에 제시 되었던 TWSVM과 비교한다.