Compiling prior knowledge into an explicit basis
ML92 Proceedings of the ninth international workshop on Machine learning
Machine Learning
Use of Contextual Information for Feature Ranking and Discretization
IEEE Transactions on Knowledge and Data Engineering
Scalable Discovery of Informative Structural Concepts Using Domain Knowledge
IEEE Expert: Intelligent Systems and Their Applications
A radial basis function approach to financial time series analysis
A radial basis function approach to financial time series analysis
A study of cross-validation and bootstrap for accuracy estimation and model selection
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
A bootstrap evaluation of the effect of data splitting on financial time series
IEEE Transactions on Neural Networks
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Inclusion of domain knowledge in a process of knowledge discovery in databases is a complex but very important part of successful knowledge discovery solutions. In real-life data mining development, nonstructured domain knowledge involvement in the data preparation phase and in the final interpretation/evaluation phase tends to dominate. This paper presents an experiment of direct domain knowledge integration in the algorithm that will search for interesting patterns in the data. In the context of stock market prediction work, a recent rule induction algorithm, PA3, was adapted to include domain theories directly in the internal rule development. Tests performed over several Portuguese stocks show a significant increase in prediction performance over the same process using the standard version of PA3. We believe that a similar methodology can be applied to other symbolic induction algorithms and in other working domains to improve the efficiency of prediction (or classification) in knowledge-intensive data mining tasks.