title: Leveraging domain information to restructure biological prediction.
abstract: BACKGROUND
  It is commonly believed that including domain knowledge in a prediction model is desirable. However, representing and incorporating domain information in the learning process is, in general, a challenging problem. In this research, we consider domain information encoded by discrete or categorical attributes. A discrete or categorical attribute provides a natural partition of the problem domain, and hence divides the original problem into several non-overlapping sub-problems. In this sense, the domain information is useful if the partition simplifies the learning task. The goal of this research is to develop an algorithm to identify discrete or categorical attributes that maximally simplify the learning task.
  
  
  RESULTS
  We consider restructuring a supervised learning problem via a partition of the problem space using a discrete or categorical attribute. A naive approach exhaustively searches all the possible restructured problems. It is computationally prohibitive when the number of discrete or categorical attributes is large. We propose a metric to rank attributes according to their potential to reduce the uncertainty of a classification task. It is quantified as a conditional entropy achieved using a set of optimal classifiers, each of which is built for a sub-problem defined by the attribute under consideration. To avoid high computational cost, we approximate the solution by the expected minimum conditional entropy with respect to random projections. This approach is tested on three artificial data sets, three cheminformatics data sets, and two leukemia gene expression data sets. Empirical results demonstrate that our method is capable of selecting a proper discrete or categorical attribute to simplify the problem, i.e., the performance of the classifier built for the restructured problem always beats that of the original problem.
  
  
  CONCLUSIONS
  The proposed conditional entropy based metric is effective in identifying good partitions of a classification problem, hence enhancing the prediction performance.
authors.affiliation: Department of Computer and Information Science, University of Mississippi, USA.
authors.forename: Xiaofei
authors.initials: X
authors.lastname: Nan
authors.forename: Gang
authors.initials: G
authors.lastname: Fu
authors.forename: Zhengdong
authors.initials: Z
authors.lastname: Zhao
authors.forename: Sheng
authors.initials: S
authors.lastname: Liu
authors.forename: Ronak Y
authors.initials: RY
authors.lastname: Patel
authors.forename: Haining
authors.initials: H
authors.lastname: Liu
authors.forename: Pankaj R
authors.initials: PR
authors.lastname: Daga
authors.forename: Robert J
authors.initials: RJ
authors.lastname: Doerksen
authors.forename: Xin
authors.initials: X
authors.lastname: Dang
authors.forename: Yixin
authors.initials: Y
authors.lastname: Chen
authors.forename: Dawn
authors.initials: D
authors.lastname: Wilkins
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D019277:Entropy; D038362:Glycogen Synthase Kinase 3 / Q000037:antagonists & inhibitors; D000071679:Glycogen Synthase Kinase 3 beta; D006801:Humans; D008954:Models, Biological; D054198:Precursor Cell Lymphoblastic Leukemia-Lymphoma / Q000188:drug therapy / Q000235:genetics; D011379:Prognosis; D043884:Receptor, Cannabinoid, CB1 / Q000378:metabolism; D043885:Receptor, Cannabinoid, CB2 / Q000378:metabolism
chemical_list: D043884:Receptor, Cannabinoid, CB1; D043885:Receptor, Cannabinoid, CB2; D000071679:Glycogen Synthase Kinase 3 beta; D038362:Glycogen Synthase Kinase 3