In this study, we present a new method for profiling the author of an anonymous English text. The aim of author profiling is to determine demographic (age, gender, region, education level) and psychological (personality, mental health) properties of the authors of a text, especially authors of user generated content in social media. To obtain the best classification, authors resort to machine l...

Software measurement has the potential to play an important role in risk management during product development. Metrics incorporated into predictive models can give advanced warning of potential risks. However, the common approach of using simple regression models, notably to predict software defects, can lead to inappropriate risk management decisions. These naïve models should be replaced wit...

In this paper, we investigate a number of Bayesian techniques for predicting 1-year- survival and making treatment selection recommendations for lung cancer. We have carried out two sets of experiments on the English Lung Cancer Dataset. For 1-year-survival prediction, the Naïve Bayes (NB) algorithm achieved an area under the curve value of 81%, outperforming the Bayesian Networks learned by th...

Under the Defense Advanced Research Projects Agency’s (DARPA) Integrated Crisis Early Warning System (ICEWS), Innovative Decisions, Inc. (IDI) constructed a Bayesian network to combine forecasts produced by a set of social science models. We used Bayesian network structure learning with political science variables to produce meaningful priors. We employed a naïve Bayes structure to aggregate th...

Accuracy is one of the major issues for a classifier. Currently there exist a range of classifiers with different degrees of accuracy directly related to computational complexity. In this paper we are presenting an approach to improve the classification accuracy of an existing PTree based Bayesian classification technique. The new approach has increased the granularity between two conditional p...

In data mining, classification is a form of data analysis that can be used to extract models describing important data classes. Two of the well known algorithms used in data mining classification are Backpropagation Neural Network (BNN) and Naïve Bayesian (NB). Bayesian approaches are a fundamentally important DM technique. Given the probability distribution, Bayes classifier can provably achie...