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On the Advantages of Multi-Input Single-Output Parallel Cascade Classifiers
Authors:James R Green  Michael J Korenberg
Institution:(1) Department of Electrical and Computer Engineering, Queen's University, Kingston, Ontario, Canada;(2) Department of Systems and Computer Engineering, Carleton University, Ottawa, Ontario, Canada;(3) Systems and Computer Engineering, 1125 Colonel By Drive, Carleton University, Ottawa, Ontario, Canada, K1S 5B6
Abstract:Parallel Cascade Identification (PCI) has been successfully applied to build dynamic nonlinear systems that address diverse challenges in the field of bioinformatics. PCI may be used to identify either single-input single-output (SISO) or multi-input single-output (MISO) models. Although SISO PCI models have typically sufficed, it has been suggested that MISO PCI systems could also be used to form bioinformatics classifiers, and indeed they were successfully applied in one study. This paper reports on the first systematic comparison of MISO and SISO PCI classifiers. Motivation for using the MISO structure is given. The construction of MISO parallel cascade models is also briefly reviewed. In order to compare the accuracy of SISO and MISO PCI classifiers, genetic algorithms are applied to optimize the model architecture on a number of equivalent single-input and multi-input biological training datasets. Through evaluation of both model structures on independent test datasets, we establish that MISO PCI is capable of building classifiers of equal accuracy to those resulting from SISO PCI models. Moreover, we discuss and illustrate the benefits of the MISO approach, including significant reduction in training and testing times, and the ability to adjust automatically the weighting of individual inputs according to information content.
Keywords:Nonlinear system identification  Parallel cascade identification  MISO  ATP-binding sites  SARAH codes  Protein sequence analysis  Exon prediction  Protein family  Genetic algorithms  Bioinformatics
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