Competition-Based Induction of Decision Models from Examples
Machine Learning - Special issue on genetic algorithms
Floating search methods in feature selection
Pattern Recognition Letters
Wrappers for performance enhancement and oblivious decision graphs
Wrappers for performance enhancement and oblivious decision graphs
Feature Selection: Evaluation, Application, and Small Sample Performance
IEEE Transactions on Pattern Analysis and Machine Intelligence
Selection of relevant features and examples in machine learning
Artificial Intelligence - Special issue on relevance
On cross validation for model selection
Neural Computation
Adaptive floating search methods in feature selection
Pattern Recognition Letters - Special issue on pattern recognition in practice VI
Mining Multiple-Level Association Rules in Large Databases
IEEE Transactions on Knowledge and Data Engineering
Feature selection toolbox software package
Pattern Recognition Letters - In memory of Professor E.S. Gelsema
Machine Learning
Search-intensive concept induction
Evolutionary Computation
A system for induction of oblique decision trees
Journal of Artificial Intelligence Research
Expert Systems with Applications: An International Journal
Making CN2-SD subgroup discovery algorithm scalable to large size data sets using instance selection
Expert Systems with Applications: An International Journal
Mining learners' behavior in accessing web-based interface
Edutainment'07 Proceedings of the 2nd international conference on Technologies for e-learning and digital entertainment
Hi-index | 12.05 |
Refuse incinerator operation poses a tremendous challenge for efficient supervision due to the highly complexity of physical and chemical mechanisms inside the systems. It is difficult to comprehend operational knowledge without thorough study and long-term on site experiments. Fortunately, many sensors are installed in incineration plants and tremendous amounts of raw data about daily practices and system states are recorded to assist operations. Without proper analysis, however, these data are not beneficial to operators. An integrated approach is adopted in the current study using feature selection and data mining techniques. Feature selection was initially applied to cope with the heavy computation burden due to the huge data set. Data dimension can be reduced by discarding redundant information and leaving only relevant features for further analysis. Data mining analysis is then utilized to build two decision tree models based on steam production and NO"x emission target attributes. Implicit incinerator system relations, represented by production rules and predicting accuracies, can be acquired from the decision tree models. Such rule-based knowledge is expected to facilitate on-site operations and enhance refuse incinerator efficiency.