Ponte Academic Journal Aug 2016, Volume 72, Issue 8 |
INCREMENTAL LEARNING DECISION TREE ALGORITHM FOR KNOWLEDGE DISCOVERY Author(s): Mohammed Moulana ,Mohammed Ali Hussain J. Ponte - Aug 2016 - Volume 72 - Issue 8 doi: 10.21506/j.ponte.2016.8.11 Abstract: A prominent learning discovery procedure is Data Mining. Decision trees are of the basic and intense decision making models in data mining. A single constraint in decision trees is the unpredictability and error rate. Motivated by human learning techniques, we suggest a decision tree structure which impersonates human adapting by performing consistent enhanced learning. In this paper, we propose a novel Incremental Learning Decision Tree (ILDT) technique taking into account human learning procedure. Far reaching trials, utilizing decision tree C4.5 as base classifier, demonstrate that the exactness of our system is similar to best in class systems.
|
Download full text: Check if you have access through your login credentials or your institution |
|
Guide for Authors
This guideline has been prepared for the authors to new submissions and after their manuscripts have been accepted |
Authors Login
We welcome refrees who would be willing to act as reviewers |
Paper Tracking
You can track your submitted article from this tab |
Editorial Board
The international editorial board is headed by Dr. Maria E. Boschi |
General Policies
Papers that are published or held by the Journal may not be published elsewhere |
Peer Review Process
Papers will be sent to three peer reviewers for evaluation |