By Topic

Data mining for case-based reasoning in high-dimensional biological domains

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$33 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

2 Author(s)
N. Arshadi ; Dept. of Comput. Sci., Toronto Univ., Ont., Canada ; I. Jurisica

Case-based reasoning (CBR) is a suitable paradigm for class discovery in molecular biology, where the rules that define the domain knowledge are difficult to obtain and the number and the complexity of the rules affecting the problem are too large for formal knowledge representation. To extend the capabilities of CBR, we propose the mixture of experts for case-based reasoning (MOE4CBR), a method that combines an ensemble of CBR classifiers with spectral clustering and logistic regression. Our approach not only achieves higher prediction accuracy, but also leads to the selection of a subset of features that have meaningful relationships with their class labels. We evaluate MOE4CBR by applying the method to a CBR system called TA3 - a computational framework for CBR systems. For two ovarian mass spectrometry data sets, the prediction accuracy improves from 80 percent to 93 percent and from 90 percent to 98.4 percent, respectively. We also apply the method to leukemia and lung microarray data sets with prediction accuracy improving from 65 percent to 74 percent and from 60 percent to 70 percent, respectively. Finally, we compare our list of discovered biomarkers with the lists of selected biomarkers from other studies for the mass spectrometry data sets.

Published in:

IEEE Transactions on Knowledge and Data Engineering  (Volume:17 ,  Issue: 8 )