Machine Learning
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
Training conditional random fields using virtual evidence boosting
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
An ensemble-based evolutionary framework for coping with distributed intrusion detection
Genetic Programming and Evolvable Machines
Mining and monitoring patterns of daily routines for assisted living in real world settings
Proceedings of the 1st ACM International Health Informatics Symposium
Discovering Activities to Recognize and Track in a Smart Environment
IEEE Transactions on Knowledge and Data Engineering
Semi-Markov conditional random fields for accelerometer-based activity recognition
Applied Intelligence
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
A two-leveled symbiotic evolutionary algorithm for clustering problems
Applied Intelligence
Activity Modeling Using Event Probability Sequences
IEEE Transactions on Image Processing
Machine Recognition of Human Activities: A Survey
IEEE Transactions on Circuits and Systems for Video Technology
On the effect of calibration in classifier combination
Applied Intelligence
Red tides prediction system using fuzzy reasoning and the ensemble method
Applied Intelligence
Hi-index | 0.00 |
Activity recognition requires further research to enable a multitude of human-centric applications in the smart home environment. Currently, the major challenges in activity recognition include the domination of major activities over minor activities, their non-deterministic nature and the lack of availability of human-understandable output. In this paper, we introduce a novel Evolutionary Ensembles Model (EEM) that values both minor and major activities by processing each of them independently. It is based on a Genetic Algorithm (GA) to handle the non-deterministic nature of activities. Our evolutionary ensemble learner generates a human-understandable rule profile to ensure a certain level of confidence for performed activities. To evaluate the EEM, we performed experiments on three different real world datasets. Our experiments show significant improvement of 0.6 % to 0.28 % in the F-measures of recognized activities compared to existing counterparts. It is expected that EEM would be a practical solution for the activity recognition problem due to its understandable output and improved accuracy.