Data mining: practical machine learning tools and techniques with Java implementations
Data mining: practical machine learning tools and techniques with Java implementations
User-context for adaptive user interfaces
Proceedings of the 12th international conference on Intelligent user interfaces
Classifier fitness based on accuracy
Evolutionary Computation
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We outline our context learning framework that harnesses information from a user's environment to learn user preferences for application actions. Within this framework, we employ XCS in a real world application for personalizing user-interface actions to individual users. Sycophant, our context aware calendaring application and research test-bed, uses XCS to adaptively generate user-preferred alarms for ten users in our study. Our results show that XCS' alarm prediction performance equals or surpasses the performance of One-R and a decision tree algorithm for all the users. XCS' average performance is close to $90$ percent on the alarm prediction task for all ten users. These encouraging results further highlight the feasibility of using XCS for predictive data mining tasks and the promise of a classifier systems based approach to personalize user interfaces.