Autogating in Flow Cytometry Data using SVM Classifiers for Bacterioplankton Identification

被引：0

作者：

Cordeiro, Elionai Moura ^{[1
]}

Wanderley, Bruno M. S. ^{[1
]}

Amorim de Araujo, Daniel Sabino ^{[1
]}

Doria Neto, Adriao Duarte ^{[2
]}

机构：

[1] Univ Fed Rio Grande do Norte, IMD, Programa Posgrad Bioinformat, Natal, RN, Brazil

[2] Univ Fed Rio Grande do Norte, CT, DCA, Dept Engn Comp & Automacao, Natal, RN, Brazil

来源：

PROCEEDINGS 2017 INTERNATIONAL CONFERENCE ON COMPUTATIONAL SCIENCE AND COMPUTATIONAL INTELLIGENCE (CSCI) | 2017年

基金：

巴西圣保罗研究基金会;

关键词：

Flow Cytometry; Autogating; Support Vector Machine; Enviromental Analysis; Machine Learning;

D O I：

10.1109/CSCI.2017.222

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

This paper shows the results of a methodology proposal for bacterioplankton identification using a machine learning approach named SVM. Samples used were taken from 19 high elevated lakes located at Pyrenees Mountains. Samples generated 74 databases after been analyzed by a specialist to serve as input to the algorithm. We observed the viability of this method with 3.35% of error in identification. Furthermore, there is no isolated direct correlation between robustness of the prediction models and high complexity of the input data but, indeed, the algorithm settings, function cost and variables choice have an important role in the performance as well.

引用

页码：1265 / 1269

页数：5

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