Decision tree algorithm (DT) is a commonly used data mining method for classification and regression. DT repeatedly divides dataset into pure subsets based on impurity measurements such as entropy Gini. Then relatively “pure” partitions consisting of observations with the (almost) same class are obtained. Gini index one representative indices measuring data. However, does not take...

Clinical decision requires reasoning in the presence of imperfect data. DTs are a well-known support tool, owing to their interpretability, fundamental safety-critical contexts such as medical diagnosis. However, learning from uncertain data leads poor generalization, and generating predictions for hinders prediction accuracy. Several methods have suggested potential probabilistic decisions at ...

Cancer is a major health issue that affects individuals all over the world. This disease has claimed lives of many people, and will continue to do so in future. Breast cancer recently surpassed cervical as most frequent among women both industrialized developing countries it now second leading cause mortality women. A high number die each year result this disease. significantly easier treat if ...

Different types of malicious attacks have been increasing simultaneously and become a serious issue for cybersecurity. Most leverage domain URLs as an attack communications medium compromise users into victim phishing or spam. We take advantage machine learning methods to detect the maliciousness automatically using three features: DNS-based, lexical, semantic features. The proposed approach ex...

Decision-tree induction algorithms are widely used in knowledge discovery and data mining, specially in scenarios where model comprehensibility is desired. A variation of the traditional univariate approach is the so-called oblique decision tree, which allows multivariate tests in its non-terminal nodes. Oblique decision trees can model decision boundaries that are oblique to the attribute axes...

Decision tree classification algorithms have significant potential for remote sensing data classification. This paper advances to adopt decision tree technologies to classify remote sensing images. First, this paper discussed the algorithms structure and the algorithms theory of decision tree. Second, C4.5 basic theory and boosting technology are explained. The decision tree technologies have s...