Improving the performance of classification models with fuzzy cognitive maps
- 1 July 2017
- conference paper
- Published by Institute of Electrical and Electronics Engineers (IEEE)
- p. 1-6
Abstract
This paper presents a novel approach to improve the accuracy of classification models used for prediction purposes by integrating a Fuzzy Cognitive Map (FCM) to produce a hybrid model. The proposed methodology first uses the FCM to discover latent correlations that exist between the data in order to form a single variable. This variable is then fed in the classification model as part of the training and testing phases to enhance its accuracy. Experimental results using datasets describing two different problems suggested noteworthy improvements in the accuracy of various classification models.Keywords
This publication has 12 references indexed in Scilit:
- Accurate occupancy detection of an office room from light, temperature, humidity and CO 2 measurements using statistical learning modelsEnergy and Buildings, 2016
- Application of fuzzy cognitive maps to electricity consumption prediction2015
- A Review of Fuzzy Cognitive Maps Research During the Last DecadeIEEE Transactions on Fuzzy Systems, 2012
- Review study on fuzzy cognitive maps and their applications during the last decade2011
- Classification and regression treesWIREs Data Mining and Knowledge Discovery, 2011
- Benchmarking main activation functions in fuzzy cognitive mapsExpert Systems with Applications, 2009
- An application of support vector machines in bankruptcy prediction modelExpert Systems with Applications, 2005
- Soft computing for crisis management and political decision making: the use of genetically evolved fuzzy cognitive mapsSoft Computing, 2004
- Support-vector networksMachine Learning, 1995
- Fuzzy cognitive mapsInternational Journal of Man-Machine Studies, 1986