@ARTICLE { Rsds701,
author = {Chouchoulas, Alexios and Shen, Qiang}, 
title = {FuREAP: a Fuzzy-Rough Estimator of Algae Populations}, 
journal = {Artificial Intelligence in Engineering}, 
number = 	{(no. 1)}, 
volume = 15, 
pages = {13-24}, 
publisher = {Elsevier Science Inc.}, 
address = {Amsterdam, The Netherlands}, 
month = {January}, 
year = 2001, 
abstract = {Concern for environmental issues has increased in recent years. Waste production influences humanity's future. The alga, an ubiquitous single-celled plant, can thrive on industrial waste, to the detriment of water clarity and human activities. To avoid this, biologists need to isolate the chemical parameters of these rapid population fluctuations. This paper proposes a Fuzzy-Rough Estimator of Algae Populations (FuREAP), a hybrid system involving Fuzzy Set and Rough Set theories that estimates the size of algae populations given certain water characteristics. Through dimensionality reduction, FuREAP significantly reduces computer time and space requirements. Also, it decreases the cost of obtaining measurements and increases runtime efficiency, making the system more viable economically. By retaining only information required for the estimation task, FuREAP offers higher accuracy than conventional rule induction systems. Finally, FuREAP does not alter the domain semantics, making the distilled knowledge human-readable. The paper addresses the problem domain, architecture and modus operandi of FuREAP, and provides and discusses detailed experimental results.}, 
keywords = {algae population estimation, fuzzy logic, rough sets, rule induction, }, 
}