ForEx++: A New Framework for Knowledge Discovery from Decision Forests
By Md. Nasim Adnan, M. Islam. 2017
30 citations recorded by the source corpus.
Decision trees are popularly used in a wide range of real world problems for both prediction and classification (logic) rules discovery. A decision forest is an ensemble of decision trees and it is often built for achieving better predictive performance compared to a single decision tree. Besides improving predictive performance, a decision forest can be seen as a pool of logic rules (rules) with great potential for knowledge discovery. However, a standard-sized decision forest usually generates a large number of rules that a user may not able to manage for effective knowledge analysis. In this paper, we propose a new, data set independent framework for extracting those rules that are comparatively more accurate, generalized and concise than others. We apply the proposed framework on rules generated by two different decision forest algorithms from some publicly available medical related data sets on dementia and heart disease. We then compare the quality of rules extracted by the proposed framework with rules generated from a single J48 decision tree and rules extracted by another recent method. The results reported in this paper demonstrate the effectiveness of the proposed framework.
Subfields: Artificial Intelligence, Computational Theory and Mathematics, Information Systems
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Rubén Morales-Menéndez
- Xiaodi Huang
- Mohammad Khubeb Siddiqui
- Houtao Deng
- Nasir Hussain
- Miron Bartosz Kursa
- Satoshi Hara
- Kohei Hayashi
In Computational Theory and Mathematics
In Information Systems
Citations in the corpus
- Interpreting tree ensembles with inTrees (2014, background): “In addition, ForEx++ [1] was proposed to extract rules from tree ensembles for knowledge discovery.”
- A review of epileptic seizure detection using machine learning classifiers (2020, methodology): “As a result, it helps in human-interpretable knowledge with high accuracy [68].”