Genetic algorithms and support vector machines for time series classification

被引：55

作者：

Eads, D ^{[1
]}

Hill, D ^{[1
]}

Davis, S ^{[1
]}

Perkins, S ^{[1
]}

Ma, JS ^{[1
]}

Porter, R ^{[1
]}

Theiler, J ^{[1
]}

机构：

[1] Los Alamos Natl Lab, Nonproliferat & Int Secur Div, Los Alamos, NM 87545 USA

来源：

APPLICATIONS AND SCIENCE OF NEURAL NETWORKS, FUZZY SYSTEMS, AND EVOLUTIONARY COMPUTATION V | 2002年 / 4787卷

关键词：

time series classification; genetic algorithm; genetic programming; support vector machines; feature selection; lightning; tornado; n-fold cross validation;

D O I：

10.1117/12.453526

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

We introduce an algorithm for classifying time series data. Since our initial application is for lightning data, we call the algorithm Zeus. Zeus is a hybrid algorithm that employs evolutionary computation for feature extraction, and a support vector machine for the final "backend" classification. Support vector machines have a reputation for classifying in high-dimensional spaces without overfitting, so the utility of reducing dimensionality with an intermediate feature selection step has been questioned. We address this question by testing Zeus on a lightning classification task using data acquired from the Fast On-orbit Recording of Transient Events (FORTE) satellite.

引用

页码：74 / 85

页数：12

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