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» Data selection for support vector machine classifiers
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CIDM
2009
IEEE
15 years 4 months ago
An empirical study of bagging and boosting ensembles for identifying faulty classes in object-oriented software
—  Identifying faulty classes in object-oriented software is one of the important software quality assurance activities. This paper empirically investigates the application of t...
Hamoud I. Aljamaan, Mahmoud O. Elish
IJCNN
2008
IEEE
15 years 4 months ago
Ranking and selecting clustering algorithms using a meta-learning approach
Abstract— We present a novel framework that applies a metalearning approach to clustering algorithms. Given a dataset, our meta-learning approach provides a ranking for the candi...
Marcílio Carlos Pereira de Souto, Ricardo B...
KDD
2002
ACM
179views Data Mining» more  KDD 2002»
15 years 10 months ago
Combining clustering and co-training to enhance text classification using unlabelled data
In this paper, we present a new co-training strategy that makes use of unlabelled data. It trains two predictors in parallel, with each predictor labelling the unlabelled data for...
Bhavani Raskutti, Herman L. Ferrá, Adam Kow...
BMCBI
2008
136views more  BMCBI 2008»
14 years 10 months ago
A comparison of machine learning algorithms for chemical toxicity classification using a simulated multi-scale data model
Background: Bioactivity profiling using high-throughput in vitro assays can reduce the cost and time required for toxicological screening of environmental chemicals and can also r...
Richard Judson, Fathi Elloumi, R. Woodrow Setzer, ...
ICC
2007
IEEE
141views Communications» more  ICC 2007»
15 years 4 months ago
A Hybrid Model to Detect Malicious Executables
— We present a hybrid data mining approach to detect malicious executables. In this approach we identify important features of the malicious and benign executables. These feature...
Mohammad M. Masud, Latifur Khan, Bhavani M. Thurai...