— Decision tree is an important method for both induction research and data mining, which is mainly used for model classification and prediction. ID3 algorithm is the most widely used algorithm in the decision tree so far. In this paper, the shortcoming of ID3's inclining to choose attributes with many values is discussed, and then a new decision tree algorithm which is improved version of ID3....

Developing two machine learning classifiers with higher accuracy for classifying income classes people earning less and a salary scale between 50,000. Decision Tree Algorithm (DTA) Naive Bayes (NBA) are the classifier mechanisms employed. On dataset of 32516 records, methods were implemented tested. Implemented each algorithm through programs performed ten rounds on both to determine distinct s...

decision-tree algorithms provide one of the most popular methodologies for symbolic knowledge acquisition. the resulting knowledge, a symbolic decision tree along with a simple inference mechanism, has been praised for comprehensibility. the most comprehensible decision trees have been designed for perfect symbolic data. classical crisp decision trees (dt) are widely applied to classification t...

panic attacks are discrete episodes of intense fear or discomfort accompanied by symptoms such as palpitations, shortness of breath, sweating, trembling, derealization and a fear of losing control or dying. although panic attacks are required for a diagnosis of panic disorder, they also occur in association with a host of other disorders listed in the 5h version of the diagnostic and statistica...

Data mining and knowledge discovery is used for discovery of hidden knowledge from large data sources. Decision trees are one of the most famous classification techniques with simple and efficient generalization technique. This paper presents a new decision tree algorithm IQ Tree for classification problem. The IQ Tree assumes using an inter quartile range conversion of attributes with C4.5 as ...

There is embryonic importance in scaling up the broadly used decision tree learning algorithms to huge datasets. Even though abundant diverse methodologies have been proposed, a fast tree growing algorithm without substantial decrease in accuracy and substantial increase in space complexity is essential to a greater extent. This paper aims at improving the performance of the SLIQ (Supervised Le...