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 select...

Information criteria have been popularly used in model selection and proved to possess nice theoretical properties. For classification, Claeskens et al. (2008) proposed support vector machine information criterion for feature selection and provided encouraging numerical evidence. Yet no theoretical justification was given there. This work aims to fill the gap and to provide some theoretical jus...

Supplier selection is an important and widely studied topic since it has significant impact on purchasing management in supply chain. Recently, support vector machine has received much more attention from researchers, while studies on supplier selection based on it are few. In this paper, a new support vector machine technology, potential support vector machine, is introduced and then combined ...

The problem of Web phishing attacks has grown considerably in recent years and phishing is considered as one of the most dangerous Web crimes, which may cause tremendous and negative effects on online business. In a Web phishing attack, the phisher creates a forged or phishing website to deceive Web users in order to obtain their sensitive financial and personal information. Several conventiona...

Feature selection in high dimensional space is the hot topic in contemporary machine learning area. In the past decade, a lot of effort has been devoted in developing various feature selection algorithms. However, each feature selection method has its own advantage for different datasets or applications. Therefore, one might face the difficulty of choosing the most suitable feature selection me...

We show that a ranking model produced by machine learning outperforms two baselines when applied to the task of selecting texts for use in creating a unit-selection synthesis voice with good domain coverage. The model learns to predict the estimated utility of an utterance based on features relating it to the utterances selected so far and a corpus of target utterances. Our analyses indicate th...