TY - GEN
T1 - Two-layered ensemble Kohonen nets for imbalanced streaming data
AU - Roy, Asim
N1 - Publisher Copyright:
© 2016 IEEE.
PY - 2016/11/14
Y1 - 2016/11/14
N2 - One of the dominant application areas of machine learning will be the industrial internet (Internet of Things), although most of the data will actually be confined to smaller internal networks and will not use the Internet. In this world of the industrial internet, streaming sensor data with rare events, such as machine or process failures and disruptions, will be the dominant form of data and it poses a particular problem for machine learning because its methods are generally not designed to handle imbalanced data. In this paper, I present a new classification algorithm for streaming imbalanced data that uses Kohonen nets at its core. To handle high-velocity streaming data, the proposed algorithm can be implemented on hardware to take advantage of massive parallelism. I provide an outline of the algorithm and some preliminary computational results.
AB - One of the dominant application areas of machine learning will be the industrial internet (Internet of Things), although most of the data will actually be confined to smaller internal networks and will not use the Internet. In this world of the industrial internet, streaming sensor data with rare events, such as machine or process failures and disruptions, will be the dominant form of data and it poses a particular problem for machine learning because its methods are generally not designed to handle imbalanced data. In this paper, I present a new classification algorithm for streaming imbalanced data that uses Kohonen nets at its core. To handle high-velocity streaming data, the proposed algorithm can be implemented on hardware to take advantage of massive parallelism. I provide an outline of the algorithm and some preliminary computational results.
KW - Classification algorithm
KW - Ensemble learning
KW - Imbalanced data
KW - Industrial internet
KW - Kohonen nets
KW - Streaming data
UR - http://www.scopus.com/inward/record.url?scp=85008248232&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85008248232&partnerID=8YFLogxK
U2 - 10.1109/CEC.2016.7748351
DO - 10.1109/CEC.2016.7748351
M3 - Conference contribution
AN - SCOPUS:85008248232
T3 - 2016 IEEE Congress on Evolutionary Computation, CEC 2016
SP - 5215
EP - 5221
BT - 2016 IEEE Congress on Evolutionary Computation, CEC 2016
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2016 IEEE Congress on Evolutionary Computation, CEC 2016
Y2 - 24 July 2016 through 29 July 2016
ER -