Please use this identifier to cite or link to this item:
|Title:||A hybrid prediction model integrating Fuzzy Cognitive Maps with Support Vector Machines||Authors:||Christodoulou, Panayiotis
Andreou, Andreas S.
|Keywords:||Classification Tree;Fuzzy Cognitive Maps;Linear Discrimination;Machine learning;Prediction;Support Vector Machine;Weighted k-NN||Category:||Computer and Information Sciences;Electrical Engineering - Electronic Engineering - Information Engineering||Field:||Natural Sciences;Engineering and Technology||Issue Date:||26-Apr-2017||Publisher:||SciTePress||Source:||19th International Conference on Enterprise Information Systems, 2017, vol. 1, pp. 554-564, 26-29 April||Link:||http://www.scitepress.org/Papers/2017/63294/63294.pdf||Conference:||International Conference on Enterprise Information Systems||Abstract:||This paper introduces a new hybrid prediction model combining Fuzzy Cognitive Maps (FCM) and Support Vector Machines (SVM) to increase accuracy. The proposed model first uses the FCM part to discover correlation patterns and interrelationships that exist between data variables and form a single latent variable. It then feeds this variable to the SVM part to improve prediction capabilities. The efficacy of the hybrid model is demonstrated through its application on two different problem domains. The experimental results show that the proposed model is better than the traditional SVM model and also outperforms other widely used supervised machine-learning techniques like Weighted k-NN, Linear Discrimination Analysis and Classification Trees.||ISBN:||978-989758247-9||DOI:||10.5220/0006329405540564||Rights:||© 2017 SCITEPRESS||Type:||Conference Papers|
|Appears in Collections:||Δημοσιεύσεις σε συνέδρια/Conference papers|
Show full item record
checked on Aug 18, 2019
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.