TY - GEN
T1 - Classification of multiple time-series via boosting
AU - Harrington, Patrick L.
AU - Rao, Arvind
AU - Hero, Alfred O.
PY - 2009
Y1 - 2009
N2 - Much of modern machine learning and statistics research consists of extracting information from high-dimensional patterns. Often times, the large number of features that comprise this high-dimensional pattern are themselves vector valued, corresponding to sampled values in a time-series. Here, we present a classification methodology to accommodate multiple time-series using boosting. This method constructs an additive model by adaptively selecting basis functions consisting of a discriminating feature's full time-series. We present the necessary modifications to Fisher Linear Discriminant Analysis and Least-Squares, as base learners, to accommodate the weighted data in the proposed boosting procedure. We conclude by presenting the performance of our proposed method against a synthetic stochastic differential equation data set and a real world data set involving prediction of cancer patient susceptibility for a particular chemoradiotherapy.
AB - Much of modern machine learning and statistics research consists of extracting information from high-dimensional patterns. Often times, the large number of features that comprise this high-dimensional pattern are themselves vector valued, corresponding to sampled values in a time-series. Here, we present a classification methodology to accommodate multiple time-series using boosting. This method constructs an additive model by adaptively selecting basis functions consisting of a discriminating feature's full time-series. We present the necessary modifications to Fisher Linear Discriminant Analysis and Least-Squares, as base learners, to accommodate the weighted data in the proposed boosting procedure. We conclude by presenting the performance of our proposed method against a synthetic stochastic differential equation data set and a real world data set involving prediction of cancer patient susceptibility for a particular chemoradiotherapy.
KW - Additive models
KW - Boosting
KW - Time-series
UR - http://www.scopus.com/inward/record.url?scp=63649122733&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=63649122733&partnerID=8YFLogxK
U2 - 10.1109/DSP.2009.4785958
DO - 10.1109/DSP.2009.4785958
M3 - Conference contribution
AN - SCOPUS:63649122733
SN - 9781424436774
T3 - 2009 IEEE 13th Digital Signal Processing Workshop and 5th IEEE Signal Processing Education Workshop, DSP/SPE 2009, Proceedings
SP - 410
EP - 415
BT - 2009 IEEE 13th Digital Signal Processing Workshop and 5th IEEE Signal Processing Education Workshop, DSP/SPE 2009, Proceedings
T2 - 2009 IEEE 13th Digital Signal Processing Workshop and 5th IEEE Signal Processing Education Workshop, DSP/SPE 2009
Y2 - 4 January 2009 through 7 January 2009
ER -