The book focuses on different variants of decision tree induction but also describes the metalearning approach in general which is applicable to other types of machine learning algorithms. The book discusses different variants of decision tree induction and represents a useful source of information to readers wishing to review some of the techniques used in decision tree learning, as well as di...

Go/no-go execution decisions are one of the most important strategic for owners during early stages construction projects. Restructuring process decision-making these may have sustainable results in long run. The purpose this paper is to establish proper go/no-go decision-tree models owners. were developed using Exhaustive Chi-square Automatic Interaction Detector (Exhaustive CHAID) and Quick, ...

background non-alcoholic fatty liver disease (nafld) is the most common form of liver disease in many parts of the world. objectives the aim of the present study was to identify the most important factors influencing nafld using a classification tree (ct) to predict the probability of nafld. patients and methods this cross-sectional study was conducted in kavar, a town in the south of fars prov...

In recent years, substantial improvements were obtained in the effectiveness of data driven algorithms to validate the mapping of items to skills, or the Q-matrix. In the current study we use ensemble algorithms on top of existing Qmatrix refinement algorithms to improve their performance. We combine the boosting technique with a decision tree. The results show that the improvements from both t...

Decision tree classification technique is one of the most popular techniques in the emerging field of data mining. There are various methods for constructing decision tree. Induced Decision tree (ID3) is the basic algorithm for constructing decision trees. After ID3 various algorithms were proposed by different researchers and authors those are extensions of ID3 algorithm. This paper contains a...

Due to the rise of technology, the possibility of fraud in different areas such as banking has been increased. Credit card fraud is a crucial problem in banking and its danger is over increasing. This paper proposes an advanced data mining method, considering both feature selection and decision cost for accuracy enhancement of credit card fraud detection. After selecting the best and most effec...

Decision tree classification techniques are currently gaining increasing impact especially in the light of the ongoing growth of data mining services. A central challenge for the decision tree classification is the identification of split rule and correct attributes. In this context, the article aims at presenting the current state of research on different techniques for classification using ob...

Classiication decision tree algorithms are used extensively for data mining in many domains such as retail target marketing, fraud detection, etc. Highly parallel algorithms for constructing classiication decision trees are desirable for dealing with large data sets in reasonable amount of time. Algorithms for building classiication decision trees have a natural concurrency, but are diicult to ...

We present an algorithm for building rule lists that is two orders of magnitude faster than previous work. Rule list algorithms are competitors for decision tree algorithms. They are associative classifiers, in that they are built from pre-mined association rules. They have a logical structure that is a sequence of IF-THEN rules, identical to a decision list or one-sided decision tree. Instead ...

Because decision trees use greedy algorithms, as a decision tree grows, lower branches have smaller number of training examples than upper branches so that the reliability of lower branches become worse than the upper branches. Therefore, a single tree may lead to unnecessary tests of attributes due to the data fragmentation. In order to improve the fragmentation problem of decision trees, a de...