Browsing by Subject "Loss functions"

Now showing 1 - 4 of 4

Open Access
Economic design of EWMA control charts based on loss function
(Elsevier, 2009) Serel, D. A.
For monitoring the stability of a process, various control charts based on exponentially weighted moving average (EWMA) statistics have been proposed in the literature. We study the economic design of EWMA-based mean and dispersion charts when a linear, quadratic, or exponential loss function is used for computing the costs arising from poor quality. The chart parameters (sample size, sampling interval, control limits and smoothing constant) minimizing the overall cost of the control scheme are determined via computational methods. Using numerical examples, we compare the performances of the EWMA charts with Shewhart over(X, -) and S charts, and investigate the sensitivity of the chart parameters to changes in process parameters and loss functions. Numerical results imply that rather than sample size or control limits, the users need to adjust the sampling interval in response to changes in the cost of poor quality.
Open Access
Energy consumption forecasting via order preserving pattern matching
(IEEE, 2014-12) Vanlı, N. Denizcan; Sayın, Muhammed O.; Yıldız, Hikmet; Göze, Tolga; Kozat, Süleyman S.
We study sequential prediction of energy consumption of actual users under a generic loss/utility function. Particularly, we try to determine whether the energy usage of the consumer will increase or decrease in the future, which can be subsequently used to optimize energy consumption. To this end, we use the energy consumption history of the users and define finite state (FS) predictors according to the relative ordering patterns of these past observations. In order to alleviate the overfitting problems, we generate equivalence classes by tying several states in a nested manner. Using the resulting equivalence classes, we obtain a doubly exponential number of different FS predictors, one among which achieves the smallest accumulated loss, hence is optimal for the prediction task. We then introduce an algorithm to achieve the performance of this FS predictor among all doubly exponential number of FS predictors with a significantly reduced computational complexity. Our approach is generic in the sense that different tying configurations and loss functions can be incorporated into our framework in a straightforward manner. We illustrate the merits of the proposed algorithm using the real life energy usage data. © 2014 IEEE.
Open Access
Qualitative test-cost sensitive classification
(Elsevier BV, 2010) Cebe, M.; Gunduz Demir, C.
This paper reports a new framework for test-cost sensitive classification. It introduces a new loss function definition, in which misclassification cost and cost of feature extraction are combined qualitatively and the loss is conditioned with current and estimated decisions as well as their consistency. This loss function definition is motivated with the following issues. First, for many applications, the relation between different types of costs can be expressed roughly and usually only in terms of ordinal relations, but not as a precise quantitative number. Second, the redundancy between features can be used to decrease the cost; it is possible not to consider a new feature if it is consistent with the existing ones. In this paper, we show the feasibility of the proposed framework for medical diagnosis problems. Our experiments demonstrate that this framework is efficient to significantly decrease feature extraction cost without decreasing accuracy. © 2010 Elsevier B.V. All rights reserved.
Open Access
Visual transformation aided contrastive learning for video-based kinship verification
(IEEE, 2017-10) Dibeklioğlu, Hamdi
Automatic kinship verification from facial information is a relatively new and open research problem in computer vision. This paper explores the possibility of learning an efficient facial representation for video-based kinship verification by exploiting the visual transformation between facial appearance of kin pairs. To this end, a Siamese-like coupled convolutional encoder-decoder network is proposed. To reveal resemblance patterns of kinship while discarding the similarity patterns that can also be observed between people who do not have a kin relationship, a novel contrastive loss function is defined in the visual appearance space. For further optimization, the learned representation is fine-tuned using a feature-based contrastive loss. An expression matching procedure is employed in the model to minimize the negative influence of expression differences between kin pairs. Each kin video is analyzed by a sliding temporal window to leverage short-term facial dynamics. The effectiveness of the proposed method is assessed on seven different kin relationships using smile videos of kin pairs. On the average, 93:65% verification accuracy is achieved, improving the state of the art. © 2017 IEEE.