Instance-Based Learning Algorithms
Machine Learning
C4.5: programs for machine learning
C4.5: programs for machine learning
Fast training of support vector machines using sequential minimal optimization
Advances in kernel methods
HOT SAX: Efficiently Finding the Most Unusual Time Series Subsequence
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
Improvements to Platt's SMO Algorithm for SVM Classifier Design
Neural Computation
Proceedings of the 10th annual conference on Genetic and evolutionary computation
Recognizing multi-user activities using wearable sensors in a smart home
Pervasive and Mobile Computing
Estimating continuous distributions in Bayesian classifiers
UAI'95 Proceedings of the Eleventh conference on Uncertainty in artificial intelligence
Application of genetic programming for multicategory patternclassification
IEEE Transactions on Evolutionary Computation
A novel approach to design classifiers using genetic programming
IEEE Transactions on Evolutionary Computation
Hi-index | 0.00 |
This paper presents an approach to recognition of human actions such as sitting, standing, walking or running by analysing the data produced by the sensors of a smart phone. The data comes as streams of parallel time series from 21 sensors. We have used genetic programming to evolve detectors for a number of actions and compared the detection accuracy of the evolved detectors with detectors built from the classical machine learning methods including Decision Trees, Naïve Bayes, Nearest Neighbour and Support Vector Machines. The evolved detectors were considerably more accurate. We conclude that the proposed GP method can capture complex interaction of variables in parallel time series without using predefined features.