The systematic test is an inevitable part of the verification and validation process for software. The most important prerequisite for a thorough software test is the design of relevant test cases, since they determine the kind and scope and hence the quality of the test. The classification-tree method and the graphical editor CTE (classification-tree editor) support the systematic design of bl...

A new method, called Illumination Dependent Colour Channels (IDCC), is presented to improve individual tree species classification. The method is based on tree crown division into illuminated and shaded parts on a digital aerial image. Colour values of both sides of the tree crown are then used in species classification. Tree crown division is achieved by comparing the projected location of an ...

Land cover mapping provides basic information for advanced science such as ecological management, biodiversity conservation, forest planning and so on. In remote sensing research, the process of creating an accurate land cover map is an important subject. Recently, there has been growing research interest in the object-oriented image classification techniques. The object-oriented image classifi...

Classification is considered as one of the building blocks in data mining problem and the major issues concerning data mining in large databases are efficiency and scalability. In this paper we propose a data classification method using AVL trees enhances the quality and stability of data mining problems. Researchers from various disciplines such as statistics, machine learning, pattern recogni...

Intrusion Detection is an efficient way of detecting the abnormal behavior of packets in the network, Although in data mining there are various effective decision tree based algorithms are implemented for the classification and detection of Intrusions in KDDCup99 Dataset. Here an efficient technique is implemented for the classification and detection of Intrusions in KDDCup99 Dataset using Feat...

Structurally, a model tree is a regression method that takes the form of a decision tree with linear regression functions instead of terminal class values at its leaves. In this study, model trees are coupled with bagging for solving classification problems. In order to apply this regression technique to classification problems, we consider the conditional class probability function and seek a ...

As a very effective method for universal purpose pattern recognition, support vector machine (SVM) was proposed for dichotomic classification problem, which exhibits a remarkable resistance to overfitting, a feature explained by the fact that it directly implements the principle of structural risk minimization. However, in real world, most of classification problems consist of multiple categori...

To cope with the computational intensity associated with classification tree analysis and the multicolinearity in the process data, a newly developed process monitoring scheme integrating classification tree and Fisher Discriminant Analysis (FDA) is developed. FDA extracts the most significant components in the original process data and achieves optimal discriminating among different faults. Cl...

Binary tree classification has been useful for classifying the whole population based on the levels of outcome variable that is associated with chosen predictors. Often we start a classification with a large number of candidate predictors, and each predictor takes a number of different cutoff values. Because of these types of multiplicity, binary tree classification method is subject to severe ...