Class-Dependent Discretization for Inductive Learning from Continuous and Mixed-Mode Data
IEEE Transactions on Pattern Analysis and Machine Intelligence
Towards identifying populations that increase the likelihood of success in genetic programming
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
EH '05 Proceedings of the 2005 NASA/DoD Conference on Evolvable Hardware
Information and Complexity in Statistical Modeling
Information and Complexity in Statistical Modeling
An Introduction to Kolmogorov Complexity and Its Applications
An Introduction to Kolmogorov Complexity and Its Applications
IEEE Transactions on Information Theory
Feature evaluation and selection with cooperative game theory
Pattern Recognition
Feature selection using dynamic weights for classification
Knowledge-Based Systems
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Commensurate indicators of diversity and fitness with desirable metric properties are derived from information distances based on Shannon entropy and Kolmogorov complexity. These metrics measure various useful distances: from an information theoretic characterization of the phenotypic behavior of a candidate model in the population to that of an ideal model of the target system's input-output relationship (fitness); from behavior of one candidate model to that of another (total information diversity); from the information about the target provided by one model to that provided by another (target relevant information diversity); from the code of one model to that of another (genotypic representation diversity); etc. Algorithms are cited for calculating the Shannon entropy based metrics from discrete data and estimating analogs thereof from heuristically binned continuous data; references are cited to methods for estimating the Kolmogorov complexity based metric. Not in the paper, but at the workshop, results will be shown of applying these algorithms to several synthetic and real world data sets: the simplest known deterministic chaotic flow; symbolic regression test functions; industrial process monitoring and control variables; and international political leadership data. Ongoing work is outlined.