Accurate activity recognition in a home setting
UbiComp '08 Proceedings of the 10th international conference on Ubiquitous computing
Evolutionary rule-based systems for imbalanced data sets
Soft Computing - A Fusion of Foundations, Methodologies and Applications - Special Issue on Evolutionary and Metaheuristics based Data Mining (EMBDM); Guest Editors: José A. Gámez, María J. del Jesús, José M. Puerta
An activity monitoring system for elderly care using generative and discriminative models
Personal and Ubiquitous Computing
IEEE Transactions on Information Technology in Biomedicine - Special section on affective and pervasive computing for healthcare
A Knowledge-Driven Approach to Activity Recognition in Smart Homes
IEEE Transactions on Knowledge and Data Engineering
Designing classifier fusion systems by genetic algorithms
IEEE Transactions on Evolutionary Computation
Learning Classifier System Ensembles With Rule-Sharing
IEEE Transactions on Evolutionary Computation
An Evolutionary Algorithm Approach to Optimal Ensemble Classifiers for DNA Microarray Data Analysis
IEEE Transactions on Evolutionary Computation
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Recognizing human activities is an active research area due to its applicability in many applications, such as assistive living and healthcare. Currently, the major challenges in activity recognition include the reliability of prediction of each classifier as they differ according to smart homes characteristics. It is not possible that one classifier always performs better than all the other classifiers for every possible situation. Therefore, in this paper, a method for activity recognition is proposed by optimizing the output of multiple classifiers with evolutionary algorithm. We combine the measurement level output of different classifiers in terms of weights for each activity class to make up the ensemble. Classifier ensemble learner generates activity rules by optimizing the prediction accuracy of weighted feature vectors to obtain significant improvement over raw classification. For the evaluation of the proposed method, experiments are performed on two real datasets from CASAS smart home. The results show that our method systematically outperforms single classifier and traditional multiclass models.