Algorithms for Molecular Biology
|
Viewing options:Associated material:Related literature:- Articles citing this article
- Other articles by authors
- Related articles/pages
Tools:Post to:
|
Abstract (provisional)
Background
Accurate classification of microarray data is critical for successful clinical diagnosis and treatment.
However, the "curse of dimensionality" problem, and noise in the data undermines the performance of many algorithms.
Method
In order to obtain a robust classifier, a novel Additive Nonparametric Margin Maximum for Case-Based Reasoning (ANMM4CBR) method is proposed in this article. ANMM4CBR employs a case-based reasoning (CBR) method for classification. CBR is a suitable paradigm for microarray analysis, where the rules that define
the domain knowledge are difficult to obtain since usually only a small number of training samples are available. Moreover, in order to select the most informative genes, we propose to perform feature selection via additively optimizing a nonparametric margin maximum criterion, which is defined based on gene pre-selection and sample clustering. Our feature selection method is very robust to noise in the data.
Results
The effectiveness of our method is demonstrated on both simulated and real data sets. We show that the ANMM4CBR method performs better than some state-of-the-art methods such as support vector machine (SVM) and k nearest neighbor (kNN), especially when the data contains a great number of noise.
Availability
The source code is attached as an additional file of this paper.
The complete article is available as a provisional PDF. The fully formatted PDF and HTML versions are in production.
|
| 
Research
on Google Scholar
