Structured induction in expert systems
Structured induction in expert systems
C4.5: programs for machine learning
C4.5: programs for machine learning
EURO-DAC '92 Proceedings of the conference on European design automation
Machine learning methods for intelligent decision support: an introduction
Decision Support Systems
Machine learning, neural and statistical classification
Machine learning, neural and statistical classification
A management decision support system for allocating housing loans
Implementing systems for supporting management decisions
Learning by discovering concept hierarchies
Artificial Intelligence
Data mining: concepts and techniques
Data mining: concepts and techniques
Knowledge management and data mining for marketing
Decision Support Systems - Knowledge management support of decision making
Understanding Decision Support Systems and Expert Systems
Understanding Decision Support Systems and Expert Systems
Making Hard Decisions with Decisiontools Suite
Making Hard Decisions with Decisiontools Suite
On Comparing Classifiers: Pitfalls toAvoid and a Recommended Approach
Data Mining and Knowledge Discovery
Problem Decomposition and the Learning of Skills
ECML '95 Proceedings of the 8th European Conference on Machine Learning
Constructing Intermediate Concepts by Decomposition of Real Functions
ECML '97 Proceedings of the 9th European Conference on Machine Learning
Induction of Concept Hierarchies from Noisy Data
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Decomposition of Multiple-Valued Functions
ISMVL '95 Proceedings of the 25th International Symposium on Multiple-Valued Logic
24-hour knowledge factory: Using Internet technology to leverage spatial and temporal separations
ACM Transactions on Internet Technology (TOIT) - Special Issue on the Internet and Outsourcing
Supporting decisions about the introduction of genetically modified crops
Proceedings of the 2008 conference on Collaborative Decision Making: Perspectives and Challenges
Extension of ICF classifiers to real world data sets
IEA/AIE'07 Proceedings of the 20th international conference on Industrial, engineering, and other applications of applied intelligent systems
Granular knowledge representation and inference using labels and label expressions
IEEE Transactions on Fuzzy Systems - Special section on computing with words
Modeling challenges with influence diagrams: Constructing probability and utility models
Decision Support Systems
Development of DEX-HOP multi-attribute decision model for preliminary hop hybrids assessment
Computers and Electronics in Agriculture
The Role of Information Resources in Enabling the 24-hour Knowledge factory
Information Resources Management Journal
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Function decomposition is a recent machine learning method that develops a hierarchical structure from class-labeled data by discovering new aggregate attributes and their descriptions. Each new aggregate attribute is described by an example set whose complexity is lower than the complexity of the initial set. We show that function decomposition can be used to develop a hierarchical multi-attribute decision model from a given unstructured set of decision examples. The method implemented in a system called HINT is experimentally evaluated on a real-world housing loans allocation problem and on the rediscovery of three hierarchical decision models. The experimentation demonstrates that the decomposition can discover meaningful and transparent decision models of high classification accuracy. We specifically study the effects of human interaction through either assistance or provision of background knowledge for function decomposition, and show that this has a positive effect on both the comprehensibility and classification accuracy.