Data mining: practical machine learning tools and techniques with Java implementations
Data mining: practical machine learning tools and techniques with Java implementations
IEEE Transactions on Pattern Analysis and Machine Intelligence
Learning Logical Definitions from Relations
Machine Learning
ILP Experiments in Detecting Traffic Problems
ECML '98 Proceedings of the 10th European Conference on Machine Learning
Detecting Traffic Problems with ILP
ILP '98 Proceedings of the 8th International Workshop on Inductive Logic Programming
A Comparison of ILP and Propositional Systems on Propositional Traffic Data
ILP '98 Proceedings of the 8th International Workshop on Inductive Logic Programming
Decision tree learning for freeway automatic incident detection
Expert Systems with Applications: An International Journal
Construct support vector machine ensemble to detect traffic incident
Expert Systems with Applications: An International Journal
nFOIL: integrating Naïve Bayes and FOIL
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 2
An empirical evaluation of bagging in inductive logic programming
ILP'02 Proceedings of the 12th international conference on Inductive logic programming
Logical and Relational Learning
Logical and Relational Learning
Traffic-incident detection-algorithm based on nonparametric regression
IEEE Transactions on Intelligent Transportation Systems
IEEE Transactions on Neural Networks
Hi-index | 12.05 |
Traffic incidents inevitably cause traffic delay and deteriorate road safety conditions. Incidents are increasing alongside the fast economic growth. Due to the rampant growth of traffic incidents, developing efficient and effective automated incident detection (AID) techniques has prompted a growing worldwide interest. In this paper, the great efforts on developing a new approach to this problem based on nFOIL, a novel inductive logic programming (ILP), are done. By way of illustration, a simulated traffic data generated from Ayer Rajah Expressway (AYE) highway in Singapore and a real traffic data collected in I-880 freeway in California are used to assess the detection performance of this approach, and performance metrics includes detection rate, false alarm rate, mean time to detection, classification rate and the area under Receiver Operating Characteristic (ROC) curve (AUC). For comparison, we conducted the experiments on neural networks and support vector machine. The experimental results showed that nFOIL is sensitive to the skewed distribution of positive and negative examples in the dataset, and we make use of two different techniques, resampling and ensemble learning, to cope with highly skewed data in the context of ILP classification problems and investigated the effect of them typicality on the performance of AID model. It is concluded that ILP based AID approach are feasible, and have a favorable performance compared to neural networks and support vector machines.