Machine Learning
Multi-sensor fusion: an Evolutionary algorithm approach
Information Fusion
IEEE Transactions on Pattern Analysis and Machine Intelligence
A random key based genetic algorithm for the resource constrained project scheduling problem
Computers and Operations Research
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 Evolutionary Ensemble-Based Method for Rule Extraction with Distributed Data
HAIS '09 Proceedings of the 4th International Conference on Hybrid Artificial Intelligence Systems
Preference model assisted activity recognition learning in a smart home environment
IROS'09 Proceedings of the 2009 IEEE/RSJ international conference on Intelligent robots and systems
A long-term evaluation of sensing modalities for activity recognition
UbiComp '07 Proceedings of the 9th international conference on Ubiquitous computing
IEEE Transactions on Evolutionary Computation
Discovering Activities to Recognize and Track in a Smart Environment
IEEE Transactions on Knowledge and Data Engineering
Machine Recognition of Human Activities: A Survey
IEEE Transactions on Circuits and Systems for Video Technology
Workshop overview for the international workshop on situation, activity and goal awareness
Proceedings of the 13th international conference on Ubiquitous computing
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Activity recognition is an emerging field that demands active research in ubiquitous computing for analyzing complex scenarios such as concurrent situation assessment and domination of major over the minor activities. In this paper, an evolutionary ensembles approach using Genetic Algorithm (GA) as a homogeneous learner has been proposed. This approach values both minor and major activities by processing each of them independently. It consists of two phases. The first phase is preprocessing of sensory data and extraction of feature vectors. Evolutionary ensembles are designed in second phase to learn different daily life activities. Finally, multiple ensembles output is pooled on central node as a complete rule profile for all performed activities. The proposed approach was evaluated on six different types of activities from Intelligent System Laboratory (ISL) dataset. It shows a higher accuracy as compared to single learner GA.