Automated feature selection for pathogen yeast Cryptococcus Neoformans

Jinshuo Liu; Dengyi Zhang; Yu Yao; Shubo Liu; Farry Hagen

doi:10.1109/ISIE.2007.4374839

Automated feature selection for pathogen yeast Cryptococcus Neoformans

Jinshuo Liu^*
, Dengyi Zhang
, Yu Yao
, Shubo Liu
, Farry Hagen

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

Abstract

Due to large storage of images, it is highly requested to analyze images in a fast and efficient way. Data mining and Pattern Recognition methods have been widely used to understand the image knowledge deeply inside. Feature selection and extraction is the pre-processing step of Data Mining. Our approach to mine from Images, deals mainly with identification and extraction of unique features for analysing the pathogen conditions of Yeast Cryptococcus Neoformans. Our automated model can determine which features can be used to identify variance pathogen condition. Different methods for extraction have been tried. Features extracted and techniques used are evaluated using the new test set images. Experimental results show that the features extracted by our automated data driven model are sufficient to identify the patterns from the Images.

Original language	English
Title of host publication	2007 IEEE International Symposium on Industrial Electronics, ISIE 2007, Proceedings
Publisher	IEEE
Pages	1580-1583
Number of pages	4
ISBN (Print)	1424407559, 9781424407552
DOIs	https://doi.org/10.1109/ISIE.2007.4374839
Publication status	Published - 2007
Externally published	Yes
Event	2007 IEEE International Symposium on Industrial Electronics, ISIE 2007 - Caixanova - Vigo, Spain Duration: 4 Jun 2007 → 7 Jun 2007

Conference

Conference	2007 IEEE International Symposium on Industrial Electronics, ISIE 2007
Country/Territory	Spain
City	Caixanova - Vigo
Period	4/06/07 → 7/06/07

Keywords

Data driven
Data mining
Feature extraction

Access to Document

10.1109/ISIE.2007.4374839

Cite this

@inproceedings{70eaf457965e46e09bf6ea63b3dcb1b7,

title = "Automated feature selection for pathogen yeast Cryptococcus Neoformans",

abstract = "Due to large storage of images, it is highly requested to analyze images in a fast and efficient way. Data mining and Pattern Recognition methods have been widely used to understand the image knowledge deeply inside. Feature selection and extraction is the pre-processing step of Data Mining. Our approach to mine from Images, deals mainly with identification and extraction of unique features for analysing the pathogen conditions of Yeast Cryptococcus Neoformans. Our automated model can determine which features can be used to identify variance pathogen condition. Different methods for extraction have been tried. Features extracted and techniques used are evaluated using the new test set images. Experimental results show that the features extracted by our automated data driven model are sufficient to identify the patterns from the Images.",

keywords = "Data driven, Data mining, Feature extraction",

author = "Jinshuo Liu and Dengyi Zhang and Yu Yao and Shubo Liu and Farry Hagen",

year = "2007",

doi = "10.1109/ISIE.2007.4374839",

language = "English",

isbn = "1424407559",

pages = "1580--1583",

booktitle = "2007 IEEE International Symposium on Industrial Electronics, ISIE 2007, Proceedings",

publisher = "IEEE",

note = "2007 IEEE International Symposium on Industrial Electronics, ISIE 2007 ; Conference date: 04-06-2007 Through 07-06-2007",

}

Liu, J, Zhang, D, Yao, Y, Liu, S & Hagen, F 2007, Automated feature selection for pathogen yeast Cryptococcus Neoformans. in 2007 IEEE International Symposium on Industrial Electronics, ISIE 2007, Proceedings., 4374839, IEEE, pp. 1580-1583, 2007 IEEE International Symposium on Industrial Electronics, ISIE 2007, Caixanova - Vigo, Spain, 4/06/07. https://doi.org/10.1109/ISIE.2007.4374839

TY - GEN

T1 - Automated feature selection for pathogen yeast Cryptococcus Neoformans

AU - Liu, Jinshuo

AU - Zhang, Dengyi

AU - Yao, Yu

AU - Liu, Shubo

AU - Hagen, Farry

PY - 2007

Y1 - 2007

N2 - Due to large storage of images, it is highly requested to analyze images in a fast and efficient way. Data mining and Pattern Recognition methods have been widely used to understand the image knowledge deeply inside. Feature selection and extraction is the pre-processing step of Data Mining. Our approach to mine from Images, deals mainly with identification and extraction of unique features for analysing the pathogen conditions of Yeast Cryptococcus Neoformans. Our automated model can determine which features can be used to identify variance pathogen condition. Different methods for extraction have been tried. Features extracted and techniques used are evaluated using the new test set images. Experimental results show that the features extracted by our automated data driven model are sufficient to identify the patterns from the Images.

AB - Due to large storage of images, it is highly requested to analyze images in a fast and efficient way. Data mining and Pattern Recognition methods have been widely used to understand the image knowledge deeply inside. Feature selection and extraction is the pre-processing step of Data Mining. Our approach to mine from Images, deals mainly with identification and extraction of unique features for analysing the pathogen conditions of Yeast Cryptococcus Neoformans. Our automated model can determine which features can be used to identify variance pathogen condition. Different methods for extraction have been tried. Features extracted and techniques used are evaluated using the new test set images. Experimental results show that the features extracted by our automated data driven model are sufficient to identify the patterns from the Images.

KW - Data driven

KW - Data mining

KW - Feature extraction

UR - https://www.scopus.com/pages/publications/50049118620

U2 - 10.1109/ISIE.2007.4374839

DO - 10.1109/ISIE.2007.4374839

M3 - Conference contribution

AN - SCOPUS:50049118620

SN - 1424407559

SN - 9781424407552

SP - 1580

EP - 1583

BT - 2007 IEEE International Symposium on Industrial Electronics, ISIE 2007, Proceedings

PB - IEEE

T2 - 2007 IEEE International Symposium on Industrial Electronics, ISIE 2007

Y2 - 4 June 2007 through 7 June 2007

ER -

Automated feature selection for pathogen yeast Cryptococcus Neoformans

Abstract

Conference

Keywords

Access to Document

Other files and links

Fingerprint

Cite this