in the present study, classification trees (cts-j48 algorithm) were used to study the occurrence of roach in rivers in flanders (belgium). the presence/absence of roach was modelled based on a set of river characteristics. the predictive performance of the cts models was assessed based on the percentage of correctly classified instances (cci) and cohen's kappa statistics. to find the best model...

Biomarker discovery using mass spectrometry (MS) data is very useful in disease detection and drug discovery. The process of biomarker discovery in MS data must start with feature selection as the number of features in MS data is extremely large (e.g. thousands) while the number of samples is comparatively small. In this study, we propose the use of genetic programming (GP) for automatic featur...

Model trees are decision trees with linear regression functions at the leaves. Although originally proposed for regression, they have also been applied successfully in classification problems. This paper studies their performance for imbalanced problems. These trees give better results that standard decision trees (J48, based on C4.5) and decision trees specific for imbalanced data (CCPDT: Clas...

Classification trees (J48) were induced to predict the habitat requirements of tench (Tinca tinca). 306 datasets were used for the given fish during 8 years in the river basins in Flanders (Belgium). The input variables consisted of the structural-habitat (width, depth, gradient slope and distance from the source) and physic chemical (pH, dissolved oxygen, water temperature and electric conduct...

classification trees (j48) were induced to predict the habitat requirements of tench (tinca tinca). 306 datasets were used for the given fish during 8 years in the river basins in flanders (belgium). the input variables consisted of the structural-habitat (width, depth, gradient slope and distance from the source) and physic chemical (ph, dissolved oxygen, water temperature and electric conduct...

The automated classification of objects from large catalogues or survey projects is an important task in many astronomical surveys. Faced with various classification algorithms, astronomers should select the method according to their requirements. Here we describe several kinds of decision trees for finding active objects by multiwavelength data, such as REPTree, Random Tree, Decision Stump, Ra...

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...

In the present study, classification trees (CTs-J48 algorithm) were used to study the occurrence of roach in rivers in Flanders (Belgium). The presence/absence of roach was modelled based on a set of river characteristics. The predictive performance of the CTs models was assessed based on the percentage of Correctly Classified Instances (CCI) and Cohen's kappa statistics. To find the best model...

This paper highlights the two machine learning approaches, viz. Rough Sets 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 approaches, we can easily and accurately predict LD in any child and also we can determine the be...

The objective of this study was to reveal the usefulness image processing and machine learning for non-destructive evaluation changes in mint leaves caused by two natural drying techniques. effects shade open-air sun on ventral side (upper surface) dorsal (lower were compared. Texture parameters extracted from digital color images converted channels R, G, B, L, a, b, X, Y, Z. Models based featu...