Persistent efforts are going on to propose more accurate decision forest building techniques. In this paper, we propose a new decision forest building technique called “Forest by Continuously Excluding Root Node (Forest CERN)”. The key feature of the proposed technique is that it strives to exclude attributes that participated in the root nodes of previous trees by imposing penalties on them to obstruct them appear in some subsequent trees. Penalties are gradually lifted in such a manner that those attributes can reappear after a while. Other than that, our technique uses bootstrap samples to generate predefined number of trees. The target of the proposed algorithm is to maximize tree diversity without impeding individual tree accuracy. We present an elaborate experimental results involving fifteen widely used data sets from the UCI Machine Learning Repository. The experimental results indicate the effectiveness of the proposed technique in most of the cases.
|Title of host publication||Advances in Knowledge Discovery and Data Mining|
|Subtitle of host publication||20th Pacific-Asia Conference, PAKDD 2016|
|Editors||R Wang, J Bailey, T Washio, J Z Huang, L Khan, G Dobbie|
|Place of Publication||Switzerland|
|Number of pages||12|
|Publication status||Published - 2016|
|Event||The 20th Pacific Asia Conference on Knowledge Discovery and Data Mining (PAKDD) 2016 - The University of Auckland, Auckland, New Zealand|
Duration: 19 Apr 2016 → 22 Apr 2016
|Name||Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)|
|Conference||The 20th Pacific Asia Conference on Knowledge Discovery and Data Mining (PAKDD) 2016|
|Period||19/04/16 → 22/04/16|
|Other||The Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD)|
is a leading international conference in the areas of knowledge discovery and data mining (KDD). It provides an international forum for researchers and industry practitioners to share their new ideas, original research results and practical development experiences from all KDD related areas, including data mining, data warehousing, machine learning, artificial intelligence, databases, statistics, knowledge engineering, visualization, decision-making systems and the emerging applications.