TY - GEN
T1 - MOA
T2 - European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML PKDD 2011
AU - Bifet, Albert
AU - Holmes, Geoff
AU - Pfahringer, Bernhard
AU - Read, Jesse
AU - Kranen, Philipp
AU - Kremer, Hardy
AU - Jansen, Timm
AU - Seidl, Thomas
PY - 2011/9/9
Y1 - 2011/9/9
N2 - Massive Online Analysis (MOA) is a software environment for implementing algorithms and running experiments for online learning from evolving data streams. MOA is designed to deal with the challenging problems of scaling up the implementation of state of the art algorithms to real world dataset sizes and of making algorithms comparable in benchmark streaming settings. It contains a collection of offline and online algorithms for classification, clustering and graph mining as well as tools for evaluation. For researchers the framework yields insights into advantages and disadvantages of different approaches and allows for the creation of benchmark streaming data sets through stored, shared and repeatable settings for the data feeds. Practitioners can use the framework to easily compare algorithms and apply them to real world data sets and settings. MOA supports bi-directional interaction with WEKA, the Waikato Environment for Knowledge Analysis. Besides providing algorithms and measures for evaluation and comparison, MOA is easily extensible with new contributions and allows for the creation of benchmark scenarios.
AB - Massive Online Analysis (MOA) is a software environment for implementing algorithms and running experiments for online learning from evolving data streams. MOA is designed to deal with the challenging problems of scaling up the implementation of state of the art algorithms to real world dataset sizes and of making algorithms comparable in benchmark streaming settings. It contains a collection of offline and online algorithms for classification, clustering and graph mining as well as tools for evaluation. For researchers the framework yields insights into advantages and disadvantages of different approaches and allows for the creation of benchmark streaming data sets through stored, shared and repeatable settings for the data feeds. Practitioners can use the framework to easily compare algorithms and apply them to real world data sets and settings. MOA supports bi-directional interaction with WEKA, the Waikato Environment for Knowledge Analysis. Besides providing algorithms and measures for evaluation and comparison, MOA is easily extensible with new contributions and allows for the creation of benchmark scenarios.
U2 - 10.1007/978-3-642-23808-6_41
DO - 10.1007/978-3-642-23808-6_41
M3 - Conference contribution
AN - SCOPUS:80052392948
SN - 9783642238079
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 617
EP - 620
BT - Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2011, Proceedings
Y2 - 5 September 2011 through 9 September 2011
ER -