Automatic selection of relevant features using Rough Set Theory for real-time situation recognition based on fuzzy SOM-based CBR

This paper investigates feature selection to discard irrelevant features for dimensionality reduction and improving situation recognition process. A situation illustrating the internal structure of a system state and its related environment is based on a large set of characteristics (features). Real-time situation recognition is still a challenge because of dealing with incremental knowledge as well as imprecise, uncertain, and redundant data (features). Investigation of relevant and key situations features could effectively enhance the situation recognition performance in terms of accuracy and computational complexity. In this paper, Case-Based Reasoning (CBR) as a problem solving approach is used for situation recognition. A fuzzy SOM-based approach by integration of Situation-Operator Modeling (SOM) and Fuzzy Logic (FL) is provided for knowledge representation in CBR process. A feature selection is realized using Rough Set Theory (RST) for data mining and uncertainty management in real-time applications. Different feature selection algorithms based on RST are applied to fuzzy SOM-based CBR. An analysis of the performance of all resulting combinations is done in terms of feature reduction and situation recognition. Finally, the proposed CBR approach is realized using experiments based on driving maneuvers conducted by a professional driving simulator. This application shows the effectiveness as well as the accuracy of the introduced approach.