Post-processing of classifier outputs in multiple classifier systems

Altincay, H
Demirekler, Mübeccel
Incomparability in classifier outputs due to the variability in their scales is a major problem in the combination of different classification systems. In order to compensate this, output normalization is generally performed where the main aim is to transform the outputs onto the same scale. In this paper, it is proposed that in selecting the transformation function, the scale similarity goal should be accomplished with two more requirements. The first one is the separability of the pattern classes in the transformed output space and the second is the compatibility of the outputs with the combination rule. A method of transformation that provides improved satisfaction of the additional requirements is proposed which is shown to improve the classification performance of both linear and Bayesian combination systems based on the use of confusion matrix based a posteriori probabilities....


Distance matrices as protein representations
Dinç, Mehmet; Atalay, Mehmet Volkan; Department of Computer Engineering (2022-9-02)
Representing protein sequences is a crucial problem in the field of bioinformatics since any data-driven model's performance is limited by the information contained in its input features. A protein's biological function is dictated by its structure and knowing a protein's structure can potentially help predict its interactions with drug candidates or predict its Gene Ontology (GO) term. Yet, off-the-shelf protein representations do not contain such information since only a small fraction of the billions of ...
Undesirable effects of output normalization in multiple classifier systems
Altincay, H; Demirekler, Mübeccel (Elsevier BV, 2003-06-01)
Incomparability of the classifier output scores is a major problem in the combination of different classification systems. In order to deal with this problem, the measurement level classifier outputs are generally normalized. However, empirical results have shown that output normalization may lead to some undesirable effects. This paper presents analyses for some most frequently used normalization methods and it is shown that the main reason for these undesirable effects of output normalization is the dimen...
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TOKSÖZ, Mehmet Altan; Ulusoy, İlkay (Institution of Engineering and Technology (IET), 2016-08-01)
The authors present a sparsity-based algorithm, basic thresholding classifier (BTC), for classification applications which is capable of identifying test samples extremely rapidly and performing high classification accuracy. They introduce a sufficient identification condition (SIC) under which BTC can identify any test sample in the range space of a given dictionary. By using SIC, they develop a procedure which provides a guidance for the selection of threshold parameter. By exploiting rapid classification...
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In this study, an Unscented Kalman Filter (UKF) algorithm is designed for estimating the attitude of a picosatellite and the in-orbit external disturbance torques. The estimation vector is formed by the satellite's attitude, angular rates, and the unknown constant components of the external disturbance torques acting on the satellite. The gravity gradient torque, residual magnetic moment, sun radiation pressure and aerodynamic drag are all included in the estimated external disturbance torque vector. The sa...
Input variable selection for hydrological predictions in ungauged catchments: with or without clustering
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A key step in data-driven environmental modelling, including for hydrological purposes, is input variable selection (IVS) to ensure that the least number of variables with minimum redundancy are used to characterize the inherent relationship between inputs and outputs. Hydrological predictions in ungauged catchments is one such area where the information on influential predictors of runoff signatures guides in understanding dominant controls of meaningful information transfer from gauged to ungauged locatio...
Citation Formats
H. Altincay and M. Demirekler, “Post-processing of classifier outputs in multiple classifier systems,” MULTIPLE CLASSIFIER SYSTEMS, pp. 159–168, 2002, Accessed: 00, 2020. [Online]. Available: