Learning classifier system: Difference between revisions

Revision as of 20:48, 14 March 2023

Revision as of 09:23, 17 May 2023

Line 13:

==== Environment ====

The environment is the source of data upon which an LCS learns. It can be an offline, finite [[Training data set|training dataset]] (characteristic of a [[data mining]], [[Statistical classification|classification]], or regression problem), or an online sequential stream of live training instances. Each training instance is assumed to include some number of ''features'' (also referred to as ''attributes'', or [[Dependent and independent variables|''independent variables'']]), and a single ''endpoint'' of interest (also referred to as the [[Class (set theory)|class]], ''action'', ''[[phenotype]]'', ''prediction'', or [[Dependent and independent variables|''dependent variable'']]). Part of LCS learning can involve [[feature selection]], therefore not all of the features in the training data need to be informative. The set of feature values of an instance is commonly referred to as the ''state''. For simplicity let's assume an example problem ___domain with [[Boolean data type|Boolean]]/[[Binary number|binary]] features and a [[Boolean data type|Boolean]]/[[Binary number|binary]] class. For Michigan-style systems, one instance from the environment is trained on each learning cycle (i.e. incremental learning). Pittsburgh-style systems perform batch learning, where rule- sets are evaluated in each iteration over much or all of the training data.

==== Rule/classifier/population ====