Instance-Based Learning Algorithms
Machine Learning
Genetic programming: on the programming of computers by means of natural selection
Genetic programming: on the programming of computers by means of natural selection
C4.5: programs for machine learning
C4.5: programs for machine learning
Event detection from time series data
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Knowledge-Based Event Detection in Complex Time Series Data
AIMDM '99 Proceedings of the Joint European Conference on Artificial Intelligence in Medicine and Medical Decision Making
HOT SAX: Efficiently Finding the Most Unusual Time Series Subsequence
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
Proceedings of the 10th annual conference on Genetic and evolutionary computation
Texture segmentation by genetic programming
Evolutionary Computation
A Field Guide to Genetic Programming
A Field Guide to Genetic Programming
Evolving automatic frame splitters
Proceedings of the 13th annual conference companion on Genetic and evolutionary computation
Estimating continuous distributions in Bayesian classifiers
UAI'95 Proceedings of the Eleventh conference on Uncertainty in artificial intelligence
Time Series Forecasting for Dynamic Environments: The DyFor Genetic Program Model
IEEE Transactions on Evolutionary Computation
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Detecting events of interest in sensor data is crucial in many areas such as medical monitoring by body sensors. Current methods often require prior domain knowledge to be available. Moreover, it is difficult for them to find complex temporal patterns existing in multi-channel data. To overcome these drawbacks, we propose a Genetic Programming (GP) based event detection methodology which can directly take raw multi-channel data as input. By applying it to three event detection tasks with various event sizes and comparing with four typical classification methods, we can see that those detectors evolved by GP can handle raw data much better than other methods. With features manually defined based on domain knowledge, our method can also be comparable with others. The analysis of evolved detectors demonstrates that distinctive characteristics of the target events are captured by these GP detectors.