Treatment Method after Discretization of Continuous Attributes Based on Attributes Importance and Samples Entropy

It has great significance to efficiently distinguish the type of the samples' data in the decision table after the discretization for the course of machine learning and data mining afterwards. This paper puts forward an annotation method of distinguishing the data type based on attributes impor...

Full description

Saved in:
Bibliographic Details
Main Authors: ShenHua Ha, Liyan Zhuang, Yuxin Zhou, Zhili Pei, Lisha Liu, Yinan Lu, Ying Kong
Format: Conference Proceeding
Language:English
Subjects:
attributes importance
decision table
Discretization
Entropy
Error analysis
Heart
Iris
Noise
samples entropy
Set theory
Online Access:Request full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:It has great significance to efficiently distinguish the type of the samples' data in the decision table after the discretization for the course of machine learning and data mining afterwards. This paper puts forward an annotation method of distinguishing the data type based on attributes importance and the samples entropy, and processed the simulation test using part of the UCI database which was artificially modified, it turns out the method is able to efficiently identify the data type with high accuracy, low misidentification rate and low reject rate.
DOI:10.1109/ICICTA.2011.293