DocumentCode :
478164
Title :
An Improved Diverse Density Algorithm for Multiple Overlapped Instances
Author :
Xu, Lei ; Guo, Mao-zu ; Zou, Quan ; Liu, Yang ; Li, Hai-Feng
Author_Institution :
Sch. of Comput. Sci. & Technol., Harbin Inst. of Technol., Harbin
Volume :
3
fYear :
2008
fDate :
18-20 Oct. 2008
Firstpage :
88
Lastpage :
91
Abstract :
Multiple-instance learning is a special machine learning algorithm between supervised learning and unsupervised learning, which has been used in medicine design, image retrieval and other research fields, and attained good performance. Diverse Density (DD) algorithm is a typical multiple- instance learning method. Due to the character of sparse positive instances, when classifying the bags which include multiple overlapped instances, some negative bags are considered as positive bags. To solve this problem, this paper proposed a new classification method, which modifies the influence strategy of the instances to the bags when classifying the bags. To verify the method, it is used to classify the real and pseudo microRNA precursors in bioinformatics, and has obtained exciting results.
Keywords :
bioinformatics; feature extraction; medical image processing; unsupervised learning; bioinformatics; diverse density algorithm; machine learning algorithm; microRNA precursors; multiple overlapped instances; multiple-instance learning; sparse positive instances; supervised learning; unsupervised learning; Algorithm design and analysis; Biomedical imaging; Computer science; Image retrieval; Layout; Machine learning; Machine learning algorithms; Proteins; Supervised learning; Unsupervised learning; Diverse Density algorithm; bioinformatics; microRNA precursors; multiple overlapped instances;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Natural Computation, 2008. ICNC '08. Fourth International Conference on
Conference_Location :
Jinan
Print_ISBN :
978-0-7695-3304-9
Type :
conf
DOI :
10.1109/ICNC.2008.517
Filename :
4667107
Link To Document :
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