A practical approach to feature selection
ML92 Proceedings of the ninth international workshop on Machine learning
C4.5: programs for machine learning
C4.5: programs for machine learning
Estimating attributes: analysis and extensions of RELIEF
ECML-94 Proceedings of the European conference on machine learning on Machine Learning
Applications of machine learning and rule induction
Communications of the ACM
Advances in knowledge discovery and data mining
Advances in knowledge discovery and data mining
Inductive logic programming and knowledge discovery in databases
Advances in knowledge discovery and data mining
Predicting equity returns from securities data
Advances in knowledge discovery and data mining
Machine Learning
Feature Selection for Knowledge Discovery and Data Mining
Feature Selection for Knowledge Discovery and Data Mining
The Role of Occam‘s Razor in Knowledge Discovery
Data Mining and Knowledge Discovery
Machine Learning
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
| Hi-index | 0.00 |
The problem of downscaling the effects of global scale climate variability into predictions of local hydrology has important implications for water resource management. Our research aims to identify predictive relationships that can be used to integrate solar and ocean-atmospheric conditions into forecasts of regional water flows. In recent work we have developed an induction technique called second-order table compression, in which learning can be viewed as a process that transforms a table consisting of training data into a second-order table (which has sets of atomic values as entries) with fewer rows by merging rows in consistency preserving ways. Here, we apply the second-order table compression technique to generate predictive models of future water inflows of Lake Okeechobee, a primary source of water supply for south Florida. We also describe SORCER, a second-order table compression learning system and compare its performance with three well-established data mining techniques: neural networks, decision tree learning and associational rule mining. SORCER gives more accurate results, on the average, than the other methods with average accuracy between 49% and 56% in the prediction of inflows discretized into four ranges. We discuss the implications of these results and the practical issues in assessing the results from data mining models to guide decision-making.