A feature selection technique for classificatory analysis
Pattern Recognition Letters
Guest editorial: Knowledge-based data analysis and interpretation
Artificial Intelligence in Medicine
Understanding the curve-polygon object
Computers and Graphics
Understanding of the concave polygon object in the shape understanding system
Computers and Graphics
Measuring similarity in feature space of knowledge entailed by two separate rule sets
Knowledge-Based Systems
Strategies to parallelize ILP systems
ILP'05 Proceedings of the 15th international conference on Inductive Logic Programming
A structural learning algorithm and its application to predictive toxicology evaluation
BVAI'05 Proceedings of the First international conference on Brain, Vision, and Artificial Intelligence
Inducing diagnostic rules for glomerular disease with the DLG machine learning algorithm
Artificial Intelligence in Medicine
Learning Classification Programs: The Genetic Algorithm Approach
Fundamenta Informaticae
| Hi-index | 0.14 |
The determination of pattern recognition rules is viewed as a problem of inductive inference, guided by generalization rules, which control the generalization process, and problem knowledge rules, which represent the underlying semantics relevant to the recognition problem under consideration. The paper formulates the theoretical framework and a method for inferring general and optimal (according to certain criteria) descriptions of object classes from examples of classification or partial descriptions. The language for expressing the class descriptions and the guidance rules is an extension of the first-order predicate calculus, called variable-valued logic calculus VL21. VL21 involves typed variables and contains several new operators especially suited for conducting inductive inference, such as selector, internal disjunction, internal conjunction, exception, and generalization. Important aspects of the theory include: 1) a formulation of several kinds of generalization rules; 2) an ability to uniformly and adequately handle descriptors (i.e., variables, functions, and predicates) of different type (nominal, linear, and structured) and of different arity (i.e., different number of arguments); 3) an ability to generate new descriptors, which are derived from the initial descriptors through a rule-based system (i.e., an ability to conduct the so called constructive induction); 4) an ability to use the semantics underlying the problem under consideration. An experimental computer implementation of the method is briefly described and illustrated by an example.