<abstract xmlns="http://www.w3.org/1999/xhtml"><p> Learning from non-stationary environments is a very popular research topic. There already exist algorithms that deal with the concept drift problem. Among them there are online or incremental learners, which process data instance by instance. Their knowledge representation can take different forms such as decision rules, which have not received enough attention in learning with concept drift. This paper reviews incremental rule-based learners designed for changing environments. It describes four of the proposed algorithms: FLORA, AQ11-PM+WAH, FACIL and VFDR. Those four solutions can be compared on several criteria, like: type of processed data, adjustment to changes, type of the maintained memory, knowledge representation, and others.</p></abstract>

Learning from non-stationary environments is a very popular research topic. There already exist algorithms that deal with the concept drift problem. Among them there are online or incremental learners, which process data instance by instance. Their knowledge representation can take different forms such as decision rules, which have not received enough attention in learning with concept drift. This paper reviews incremental rule-based learners designed for changing environments. It describes four of the proposed algorithms: FLORA, AQ11-PM+WAH, FACIL and VFDR. Those four solutions can be compared on several criteria, like: type of processed data, adjustment to changes, type of the maintained memory, knowledge representation, and others.

Incremental Rule-Based Learners for Handling Concept Drift: An Overview

Institute of Computing Science, Poznań University of Technology, 60-965 Poznań, Poland

Foundations of Computing and Decision Sciences

{"article-title":"Incremental Rule-Based Learners for Handling Concept Drift: An Overview"}

Learning from non-stationary environments is a very popular research topic. There already exist algorithms that deal with the concept drift problem. Among them...

Incremental Rule-Based Learners for Handling Concept Drift: An Overview

Online veröffentlicht: 23. Feb. 2013

Seitenbereich: 35 - 65

DOI: https://doi.org/10.2478/v10209-011-0020-y

Schlüsselwörter
data mining, decision rules, rule-based classifiers, incremental learning, online learning, data streams, concept drift, non-stationary environment, overview

This content is open access.

Incremental Rule-Based Learners for Handling Concept Drift: An Overview

Online veröffentlicht: 23. Feb. 2013

Seitenbereich: 35 - 65

DOI: https://doi.org/10.2478/v10209-011-0020-y

Schlüsselwörterdata mining, decision rules, rule-based classifiers, incremental learning, online learning, data streams, concept drift, non-stationary environment, overview

This content is open access.

Schlüsselwörter
data mining, decision rules, rule-based classifiers, incremental learning, online learning, data streams, concept drift, non-stationary environment, overview