Classification Method Incorporating Decision Tree with Particle Swarm Optimization

This study attempts to develop a new method that could be used to handle the problem of finding the cut point or interval of continuous-valued attribute in decision tree, and to reach the following objectives: 1. The decision tree algorithm can handle the data that combines both the nominal attribut...

Full description

Saved in:
Bibliographic Details
Main Authors: Chien-Lung Chan, Cheng-Yang Lee, Nan-Ping Yang, Sheng-Yuan Shen
Format: Conference Proceeding
Language:English
Subjects:
Algorithm design and analysis
Classification algorithms
continuous-valued attribute
decision tree
Decision trees
Insurance
Particle swarm optimization
Training data
Online Access:Request full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:This study attempts to develop a new method that could be used to handle the problem of finding the cut point or interval of continuous-valued attribute in decision tree, and to reach the following objectives: 1. The decision tree algorithm can handle the data that combines both the nominal attribute and continuous-valued attribute. 2. The decision tree algorithm has less nodes and branches in the situation that accuracy of prediction has no obvious change. 3. The decision tree algorithm can generate better rules with multi-interval division of attribute.
DOI:10.1109/ICGEC.2011.59