Using Model Trees for Classification
Machine Learning
Machine Learning
Machine Learning
Generating Accurate Rule Sets Without Global Optimization
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Combining clustering and co-training to enhance text classification using unlabelled data
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Rotation Forest: A New Classifier Ensemble Method
IEEE Transactions on Pattern Analysis and Machine Intelligence
Pattern Recognition, Third Edition
Pattern Recognition, Third Edition
Using Clustering and Co-5raining to Boost Classification Performance
ICTAI '07 Proceedings of the 19th IEEE International Conference on Tools with Artificial Intelligence - Volume 02
The WEKA data mining software: an update
ACM SIGKDD Explorations Newsletter
Automatic Cluster Selection Using Index Driven Search Strategy
AI*IA '09: Proceedings of the XIth International Conference of the Italian Association for Artificial Intelligence Reggio Emilia on Emergent Perspectives in Artificial Intelligence
An AI tool for the petroleum industry based on image analysis and hierarchical clustering
IDEAL'09 Proceedings of the 10th international conference on Intelligent data engineering and automated learning
Clustering and classification techniques for blind predictions of reservoir facies
AI*IA'11 Proceedings of the 12th international conference on Artificial intelligence around man and beyond
Spot detection in images with noisy background
ICIAP'11 Proceedings of the 16th international conference on Image analysis and processing: Part I
Ranking and selection of unsupervised learning marketing segmentation
Knowledge-Based Systems
| Hi-index | 12.05 |
Cascade of unsupervised and supervised learning algorithms are suitable in all those problems where there are large unlabelled input datasets and the underlying data structure is hidden and not clearly defined. In petroleum geology the understanding and characterization of reservoirs needs integration of different subsurface data in order to create reliable reservoir models. The large amount of data for each well and the presence of different wells to be simultaneously analysed make this task both complex and time consuming. In this scenario, the development of reliable characterization methods is of prime importance in order to help the geologist and reduce the subjectivity of data interpretation. In this paper, we propose a novel interpretation system based on the use of unsupervised and supervised learning techniques in cascade. Using unsupervised algorithm the domain expert identifies relevant clusters that will be used as classes in the following step, in order to learn a classifier to be applied to new instances and wells. We test the approach over five real well dataset using different evaluating techniques. Main advantages of this approach are the ability to manage and use a large amount of data simultaneously and the reduction in interpretation time of a group of wells.