The overall decision tree induction algorithm is explained as well as different methods for the most important functions of a decision tree induction algorithm, such as attribute selection, attribute discretization, and pruning, developed by us and others. We explain how the learnt model can be ...
The basic principle, the advantageous properties of decision tree induction methods, and a description of the representation of decision trees so that a user can understand and describe the tree in a common way is given first. The overall decision tree induction algorithm is explained as well as...
As a result, the C4.5 classifier algorithm shows the highest accuracy of model for the dataset. Consequently, the possible talent rules are generated based on C4.5 classifier especially for the talent forecasting purposes. 展开 关键词: Talent Forecasting Data Mining Classification Decision tree C4.5...
The integration of Landsat TM and environmental GIS data sets using artificial intelligence rule-induction and decision-tree analysis is shown to facilitat... BG Lees,K Ritman - 《Environmental Management》 被引量: 338发表: 1991年 Decision-tree-based symbolic rule induction system for text categor...
A method and apparatus are disclosed for generating a decision tree classifier from a training set of records. The method comprises the steps of: pre-sorting the records based on each numeric record attribute, creating a decision tree breadth-first, and pruning the tree based on the MDL princip...
Using and comparing different decision tree classification techniques for mining ICDDR,B Hospital Surveillance data In this research we have used decision tree induction algorithm on Hospital Surveillance data to classify admitted patients according to their critical con... RFRM Hasan - 《Expert Systems ...
This report describes two such approaches, one being incremental tree induction, and the other being non-incremental tree induction using a measure of tree quality instead of test quality. The algorithm ITI for incremental tree induction includes several significant advances from its predecessor ID5R,...
Decision Tree Induction Algorithm Wrappers A wrapper is written around Orange C4.5, sklearn CART, GUIDE and QUEST. The returned object is a Decision Tree, which can be found in decisiontree.py. Moreover, different methods are available on this decision tree: classify new, unknown samples; visu...
We propose two new heuristics in decision tree algorithm design, namely removal of insignificant attributes in induction process at each tree node, and usage of combined strategy for generating possible splits for decision trees, utilizing several ways of splitting together, which experimentally showed ...
When used with noisy rather than deterministic data, the method involves th... J Mingers - 《Machine Learning》 被引量: 898发表: 1989年 Look-ahead based fuzzy decision tree induction Decision tree induction is typically based on a top-down greedy algorithm that makes locally optimal decisions ...