In this paper we present an improvement of the precision of classification algorithm results. Two various approaches are known: bagging and boosting. This paper describes a set of experiments with bagging and boosting methods. Our use of these methods aims at classification algorithms generating decision trees. Results of performance tests focused on the use of the bagging and boosting methods ...

The framework of this paper is supervised learning using classification trees. Two types of variables play a role in the definition of the classification rule, namely a response variable and a set of predictors. The tree classifier is built up by a recursive partitioning of the prediction space such to provide internally homogeneous groups of objects with respect to the response classes. In the...

We go beyond the level of individual sentences applying parse tree kernels to paragraphs. We build a set of extended trees for a paragraph of text from the individual parse trees for sentences and learn short texts such as search results and social profile postings to take advantage of additional discourse-related information. Extension is based on coreferences and rhetoric structure relations ...

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...

In this paper, we address the problem of probability estimation of decision trees. This problem has received considerable attention in the areas of machine learning and data mining, and techniques to use tree models as probability estimators have been suggested. We make a comparative study of six well-known class probability estimation methods, measured by classification accuracy, AUC and Condi...

This paper highlights the study of two classification methods, Rough Sets Theory (RST) and Decision Trees (DT), for the prediction of Learning Disabilities (LD) in school-age children, with an emphasis on applications of data mining. Learning disability prediction is a very complicated task. By using these two classification methods we can easily and accurately predict LD in any child. Also, we...

A common form of prior knowledge in economic modelling concerns the monotonicity of relations between the dependent and explanatory variables. Monotonicity may also be an important requirement with a view toward explaining and justifying decisions based on such models. We explore the use of monotonicity constraints in classification tree algorithms. We present an application of monotonic classi...

Applications of learning algorithms in knowledge discovery are promising and relevant area of research. It is offering new possibilities and benefits in real-world applications, helping us understand better mechanisms of our own methods of knowledge acquisition. Decision trees is one of learning algorithms which posses certain advantages that make it suitable for discovering the classification ...

This paper presents the results of experiments in which machine learning techniques were applied to the problem of determining regional dialect boundaries. Specifically, decision trees classification and k-means clustering were applied to a corpus of phonetic measurements taken from a large survey of North American English vowels. Pairwise classification and clustering experiments were done for...