This paper presents the use of a genetic programming (GP) system called STROGANOFF and a multilayer perceptron for thalassemic patient classification. The interested problem covers the test samples from normal subjects and that from different types of thalassemic patient and thalassemic trait. The features, which are the characteristics of red blood cell, Thalassemic Patient Classification Usin...

This paper describes a set of experiments with bagging – a method, which can improve results of classification algorithms. Our use of this method aims at classification algorithms generating decision trees. Results of performance tests focused on the use of the bagging method on binary decision trees are presented. The minimum number of decision trees, which enables an improvement of the classi...

Classification is important problem in data mining. Given a data set, classifier generates meaningful description for each class. Decision trees are most effective and widely used classification methods. There are several algorithms for induction of decision trees. These trees are first induced and then prune subtrees with subsequent pruning phase to improve accuracy and prevent overfitting. In...

The alternating decision tree (ADTree) is a successful classification technique that combines decision trees with the predictive accuracy of boosting into a set of interpretable classification rules. The original formulation of the tree induction algorithm restricted attention to binary classification problems. This paper empirically evaluates several wrapper methods for extending the algorithm...

This paper describes boosting – a method, which can improve results of classification algorithms. The use of this method aims at classification algorithms generating decision trees. A modification of the AdaBoost algorithm was implemented. Results of performance tests focused on the use of the boosting method on binary decision trees are presented. The minimum number of decision trees, which en...

Classification and regression trees are becoming increasingly popular for partitioning data and identifying local structure in small and large datasets. Classification trees include those models in which the dependent variable (the predicted variable) is categorical. Regression trees include those in which it is continuous. This paper discusses pitfalls in the use of these methods and highlight...

We present a new classification algorithm that combines three properties: It generates decision trees, which proved a valuable and intelligible tool for classification and generalization of data; it utilizes fuzzy logic, that provides for a fine grained description of classified items adequate for human reasoning; and it is incremental, allowing rapid alternation of classification and learning ...

In this study, we examined the effect of example cases with confounding values on decision trees constructed for six otoneurological diseases involving vertigo. The six diseases were benign positional vertigo, Menière’s disease, sudden deafness, traumatic vertigo, vestibular neuritis, and vestibular schwannoma. Patient cases with confounding values were inserted into original vertigo data and d...

Classification trees, usually used as a nonlinear, nonparametric classification method, can also provide a powerful framework for comparing, assessing, and combining information from different expert systems, by treating their predictions as the independent variables in a classification tree analysis. This paper discusses the applied problem of classifying chemicals as human carcinogens. It sho...