Prediction of dementia by increasing subspace size in rank forest

Salyean Giri, Abeer Alsadoon, Chandana Withana, Salih Ali, A. Elchouemic

Research output: Book chapter/Published conference paperConference paperpeer-review


Decision tree and decision forest are widely used as the best practices for knowledge discovery and prediction process. However, there is still room for improvement in terms of accuracy and processing time. The aim of this study is to improve the accuracy of knowledge discovery and uses extracted rule sets from knowledge discovery process to build a highly accurate prediction model with minimum processing time. We use a different combination of state of art algorithms like Random forest, rank forest and sub-spacing to discover interesting rules for knowledge discovery by using probabilistic values for splitting attributes in a tree and increase sub-space in a lower node to include all attributes that have potential information for knowledge discovery. Then use that knowledge to build a model for future prediction. Adding a different combination of decision trees and forest algorithms on the framework proposed by Zahid and Adnan resulted in the increased accuracy of knowledge discovery. Further research shows that use of probabilistic values for splitting attributes in a tree and increasing sub-space in a lower node of trees includes all attributes that have potential information for knowledge discovery. Thus, the proposed prediction model has decreased the processing time from 0.82 seconds to 0.11 seconds and increases the accuracy from 71.6% to 83.1%. In this paper, the different combination of rank forest and increased size of subspace is used in top of state of art. This approach has overcome the limitation of state of art and does not extract any partial rules and generate highly accurate rule sets with low processing time.
Original languageEnglish
Title of host publicationProceedings of the 2018 IEEE 8th annual computing and communication workshop and conference (CCWC), 8-10 Jan. 2018
Place of PublicationUSA
PublisherIEEE, Institute of Electrical and Electronics Engineers
Number of pages6
ISBN (Electronic)9781538646496
ISBN (Print)9781538646502 (Print on demand)
Publication statusPublished - 27 Feb 2018
Event8th IEEE Annual Computing and Communication Workshop and Conference: IEEE-CCWC 2018 - University of Nevada, Las Vegas, United States
Duration: 08 Jan 201810 Jan 2018 (Archived page) (Conference website)


Conference8th IEEE Annual Computing and Communication Workshop and Conference
Country/TerritoryUnited States
CityLas Vegas
OtherWe are proud to present IEEE CCWC 2018 which will provide an opportunity for researchers, educators and students to discuss and exchange ideas on issues, trends, and developments in Computing and Communication. The conference aims to bring together scholars from different disciplinary backgrounds to emphasize dissemination of ongoing research in the fields of in Computing and Communication. Contributed papers are solicited describing original works in the above mentioned fields and related technologies. The conference will include a peer-reviewed program of technical sessions, special sessions, business application sessions, tutorials, and demonstration sessions. All accepted papers will be presented during the parallel sessions of the Conference and papers will be submitted for publication at IEEE Xplore Digital Library.
This conference will also promote an intense dialogue between academia and industry to bridge the gap between academic research, industry initiatives, and governmental policies. This is fostered through panel discussions, keynotes, invited talks and industry exhibits where academia is exposed to state-of-practice and results from trials and interoperability experiments. The industry in turn benefits by exposure to leading-edge research in networking as well as the opportunity to communicate with academic researchers regarding practical problems that require further research.
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