Progressive advancements in the data stream mining have paved way for many algorithms to solve the problem of concept drift, a serious problem due to the wavering nature of the real time concepts. Concepts tend to change with time therefore concept drift is unavoidable in data stream mining but efficient algorithms can be designed to detect the concept drift and solve the problem. This paper pr...

Introduction: Lack of a proper method for early detection and diagnostic errors in medicine are some fundamental problems in treating cancer. Data analysis techniques may significantly help early diagnosis. The current study aimed at applying and evaluating neural networks and decision tree algorithm on breast cancer patients’ data for early cancer prediction. Methods: In the current stu...

Introduction: Lack of a proper method for early detection and diagnostic errors in medicine are some fundamental problems in treating cancer. Data analysis techniques may significantly help early diagnosis. The current study aimed at applying and evaluating neural networks and decision tree algorithm on breast cancer patients’ data for early cancer prediction. Methods: In the current stu...

forest types mapping, is one of the most necessary elements in the forest management and silviculture treatments. traditional methods such as field surveys are almost time-consuming and cost-intensive. improvements in remote sensing data sources and classification –estimation methods are preparing new opportunities for obtaining more accurate forest biophysical attributes maps. this research co...

Lazy learning algorithms, exemplified by nearestneighbor algorithms, do not induce a concise hypothesis from a given training set; the inductive process is delayed until a test instance is given. Algorithms for constructing decision trees, such as C4.5, ID3, and CART create a single “best” decision tree during the training phase, and this tree is then used to classify test instances. The tests ...

Our investigation aims at detecting network intrusions using decision tree algorithms. Large differences in prior class probabilities of intrusion data have been reported to hinder the performance of decision trees. We propose to replace the Shannon entropy used in tree induction algorithms with a Kolmogorov Smirnov splitting criterion which locates a Bayes optimal cutpoint of attributes. The K...

Previous research has shown that a technique called error correcting output coding ECOC can dramatically improve the classi cation accuracy of supervised learning algorithms that learn to classify data points into one of k classes This paper presents an empirical investigation of why the ECOC technique works particularly when employed with decision tree learning methods It concludes that an imp...