A unified view of rank-based decision combination

Date

2000-09-07

Author

Saranlı, Afşar

Metadata

Show full item record

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Item Usage Stats

175
views

0
downloads

This study presents a theoretical investigation of the rank-based multiple classifier decision problem for closed-set pattern classification. The case with classifier raw outputs in the form of candidate class rankings is considered and formulated as a discrete optimization problem with the objective function being the total probability of correct decision. The problem has a global optimum solution but is of prohibitive dimensionality. We present a partitioning formalism under which this dimensionality can be reduced by incorporating our prior knowledge about the problem domain and the structure of the training data. The formalism can effectively explain a number of rank-based combination approaches successfully used in the literature one of which is discussed.

Subject Keywords

Classifiers

URI

https://hdl.handle.net/11511/55056

Conference Name

15th International Conference on Pattern Recognition (ICPR-2000)

Collections

Department of Electrical and Electronics Engineering, Conference / Seminar

Suggestions

OpenMETU
Core

A statistical unified framework for rank-based multiple classifier decision combination Saranlı, Afşar (2001-04-01) This study presents a theoretical investigation of the rank-based multiple classifier decision combination problem, with the aim of providing a unified framework to understand a variety of such systems. The combination of the decisions of more than one classifiers with the aim of improving overall system performance is a concept of general interest in pattern recognition, as a viable alternative to designing a single sophisticated classifier. The problem of combining the classifier decisions in the raw form...
An expert system for the differential diagnosis of erythemato-squamous diseases Guvenir, HA; Emeksiz, N (2000-01-01) This paper presents an expert system for differential diagnosis of erythemato-squamous diseases incorporating decisions made by three classification algorithms: nearest neighbor classifier, naive Bayesian classifier and voting feature intervals-5. This tool enables doctors to differentiate six types of erythemato-squamous diseases using clinical and histopathological parameters obtained from a patient. The program also gives explanations for the classifications of each classifier. The patient records are al...
A sequential classification algorithm for autoregressive processes Otlu, Güneş; Candan, Çağatay; Çiloğlu, Tolga; Department of Electrical and Electronics Engineering (2011) This study aims to present a sequential method for the classification of the autoregressive processes. Different from the conventional detectors having fixed sample size, the method uses Wald’s sequential probability ratio test and has a variable sample size. It is shown that the suggested method produces the classification decisions much earlier than fixed sample size alternative on the average. The proposed method is extended to the case when processes have unknown variance. The effects of the unknown pro...
A novel approach for small sample size family-based association studies: sequential tests İlk Dağ, Özlem; Dungul, Dilay Ciglidag; ÖZDAĞ, Hilal; İLK, HAKKI GÖKHAN (Springer Science and Business Media LLC, 2011-08-01) In this paper, we propose a sequential probability ratio test (SPRT) to overcome the problem of limited samples in studies related to complex genetic diseases. The results of this novel approach are compared with the ones obtained from the traditional transmission disequilibrium test (TDT) on simulated data. Although TDT classifies single-nucleotide polymorphisms (SNPs) to only two groups (SNPs associated with the disease and the others), SPRT has the flexibility of assigning SNPs to a third group, that is,...
A Novel Computational Method to Calculate Nonlinear Normal Modes of Complex Structures Samandarı, Hamed; Ciğeroğlu, Ender (2019-01-31) In this study, a simple and efficient computational approach to obtain nonlinear normal modes (NNMs) of nonlinear structures is presented. Describing function method (DFM) is used to capture the nonlinear internal forces under periodic motion. DFM has the advantage of expressing the nonlinear internal force as a nonlinear stiffness matrix multiplied by a displacement vector, where the off-diagonal terms of the nonlinear stiffness matrix can provide a comprehensive knowledge about the coupling between the mo...

Citation Formats

A. Saranlı, “A unified view of rank-based decision combination,” presented at the 15th International Conference on Pattern Recognition (ICPR-2000), Barcelona, SPAIN, 2000, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/55056.