Qualified predictions for microarray and proteomics pattern diagnostics with confidence machines.
We focus on the problem of prediction with confidence and describe a recently developed learning algorithm called transductive confidence machine for making qualified region predictions. Its main advantage, in comparison with other classifiers, is that it is well-calibrated, with number of prediction errors strictly controlled by a given predefined confidence level. We apply the transductive confidence machine to the problems of acute leukaemia and ovarian cancer prediction using microarray and proteomics pattern diagnostics, respectively. We demonstrate that the algorithm performs well, yielding well-calibrated and informative predictions whilst maintaining a high level of accuracy.
Bellotti
Tony
T
Computer Learning Research Centre, Royal Holloway, University of London, Egham, Surrey TW20 0EX, United Kingdom. tony@cs.rhul.ac.uk
Luo
Zhiyuan
Z
Gammerman
Alex
A
Van Delft
Frederick W
FW
Saha
Vaskar
V
D000465:Algorithms; D002648:Child; D005260:Female; D006801:Humans; D007938:Leukemia / Q000175:diagnosis* / Q000235:genetics; D016571:Neural Networks, Computer; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*; D010051:Ovarian Neoplasms / Q000175:diagnosis* / Q000235:genetics; D040901:Proteomics