Graph-Based Algorithms for Boolean Function Manipulation
IEEE Transactions on Computers
International Journal of Man-Machine Studies - Special Issue: Knowledge Acquisition for Knowledge-based Systems. Part 5
Boolean Feature Discovery in Empirical Learning
Machine Learning
Designing Storage Efficient Decision Trees
IEEE Transactions on Computers
Symbolic Boolean manipulation with ordered binary-decision diagrams
ACM Computing Surveys (CSUR)
IEEE Transactions on Knowledge and Data Engineering
The Item-Set Tree: A Data Structure for Data Mining
DaWaK '99 Proceedings of the First International Conference on Data Warehousing and Knowledge Discovery
Global Data Analysis and the Fragmentation Problem in Decision Tree Induction
ECML '97 Proceedings of the 9th European Conference on Machine Learning
Building Decision Trees Using Functional Dependencies
ITCC '04 Proceedings of the International Conference on Information Technology: Coding and Computing (ITCC'04) Volume 2 - Volume 2
ICDM '04 Proceedings of the Fourth IEEE International Conference on Data Mining
Introduction to Data Mining, (First Edition)
Introduction to Data Mining, (First Edition)
Generating production rules from decision trees
IJCAI'87 Proceedings of the 10th international joint conference on Artificial intelligence - Volume 1
Data Mining with Decision Trees: Theroy and Applications
Data Mining with Decision Trees: Theroy and Applications
On constructing semantic decision tables
DEXA'07 Proceedings of the 18th international conference on Database and Expert Systems Applications
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This paper presents a generalized theory for capturing and manipulating classification information. We define decision algebra which models decision-based classifiers as higher order decision functions abstracting from implementations using decision trees (or similar), decision rules, and decision tables. As a proof of the decision algebra concept we compare decision trees with decision graphs, yet another instantiation of the proposed theoretical framework, which implement the decision algebra operations efficiently and capture classification information in a non-redundant way. Compared to classical decision tree implementations, decision graphs gain learning and classification speed up to 20% without accuracy loss and reduce memory consumption by 44%. This is confirmed by experiments.