Forecasting stock market behavior has received tremendous attention from investors, and researchers for a very long time due to its potential profitability. Predicting is regarded as one of the extremely challenging applications series forecasting. While there divided opinion on efficiency markets, numerous empirical studies which are widely accepted have shown that predictable some extent. Sta...

In this paper we study methods that combine multiple classification models learned over separate data sets in a distributed database setting. Numerous studies posit that such approaches provide the means to efficiently scale learning to large datasets, while also boosting the accuracy of individual classifiers. These gains, however, come at the expense of an increased demand for run-time system...

In this work, we investigate the use of two kinds of machine learning techniques Decision Trees and Naive Bayes applied to the problem of spam classification. We first consider building a decision tree for this purpose and then, investigate building an ensemble of decision trees using boosting. Decision trees are seen to give fairly good classification accuracy of around 92% and with the use of...

Construction of a decision tree is a well researched problem in data mining. Mining of streaming data is a very useful and necessary application. Algorithms such as VFDT and CVFDT are used for decision tree construction, but as a lot of new examples are added, a new optimal model needs to be constructed. Here in this paper, we have provided an algorithm for decision tree construction which uses...

Decision tree construction is a well studied problem in data mining. Recently, there has been much interest in mining streaming data. Algorithms like VFDT and CVFDT exist for the construction of a decision tree but, as the new examples are added, a new model has to be generated. In this paper, we have given an algorithm for construction of a decision tree that uses discriminant analysis, to cho...

We compare the use of three algorithms for performing automated morphological galaxy classiÐcation using a sample of 800 galaxies. ClassiÐers are created using a single training set as well as bootstrap replicates of the training set, producing an ensemble of classiÐers. We use a Naive Bayes classiÐer, a neural network trained with backpropagation, and a decision-tree induction algorithm with p...

Precipitation data are important for the fields of hydrology and meteorology, and are fundamental for ecosystem monitoring and climate change research. Satellite-based precipitation products are already able to provide high temporal resolution precipitation information at a global level. However, the coarse spatial resolution has restricted their use in regional level studies. In this study, mo...