Qualitative test-cost sensitive classification

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

Qualitative test-cost sensitive classification

Ankara : The Department of Computer Engineering and the Institute of Engineering and Science of Bilkent University, 2008.Thesis (Master's) -- Bilkent University, 2008.Includes bibliographical references leaves 69-72.Decision making is a procedure for selecting the best action among several alternatives. In many real-world problems, decision has to be taken under the circumstances in which one has to pay to acquire information. In this thesis, we propose a new framework for test-cost sensitive classification that considers the misclassification cost together with the cost of feature extraction, which arises from the effort of acquiring features. This proposed framework introduces two new concepts to test-cost sensitive learning for better modeling the real-world problems: qualitativeness and consistency. First, this framework introduces the incorporation of qualitative costs into the problem formulation. This incorporation becomes important for many real world problems, from finance to medical diagnosis, since the relation between the misclassification cost and the cost of feature extraction could be expressed only roughly and typically in terms of ordinal relations for these problems. For example, in cancer diagnosis, it could be expressed that the cost of misdiagnosis is larger than the cost of a medical test. However, in the test-cost sensitive classification literature, the misclassification cost and the cost of feature extraction are combined quantitatively to obtain a single loss/utility value, which requires expressing the relation between these costs as a precise quantitative number. Second, the proposed framework considers the consistency between the current information and the information after feature extraction to decide which features to extract. For example, it does not extract a new feature if it brings no new information but just confirms the current one; in other words, if the new feature is totally consistent with the current information. By doing so, the proposed framework could significantly decrease the cost of feature extraction, and hence, the overall cost without decreasing the classification accuracy. Such consistency behavior has not been considered in the previous test-cost sensitive literature. We conduct our experiments on three medical data sets and the results demonstrate that the proposed framework significantly decreases the feature extraction cost without decreasing the classification accuracy.Cebe, MüminM.S

Quantifying Facial Age by Posterior of Age Comparisons

We introduce a novel approach for annotating large quantity of in-the-wild facial images with high-quality posterior age distribution as labels. Each posterior provides a probability distribution of estimated ages for a face. Our approach is motivated by observations that it is easier to distinguish who is the older of two people than to determine the person's actual age. Given a reference database with samples of known ages and a dataset to label, we can transfer reliable annotations from the former to the latter via human-in-the-loop comparisons. We show an effective way to transform such comparisons to posterior via fully-connected and SoftMax layers, so as to permit end-to-end training in a deep network. Thanks to the efficient and effective annotation approach, we collect a new large-scale facial age dataset, dubbed `MegaAge', which consists of 41,941 images. Data can be downloaded from our project page mmlab.ie.cuhk.edu.hk/projects/MegaAge and github.com/zyx2012/Age_estimation_BMVC2017. With the dataset, we train a network that jointly performs ordinal hyperplane classification and posterior distribution learning. Our approach achieves state-of-the-art results on popular benchmarks such as MORPH2, Adience, and the newly proposed MegaAge.Comment: To appear on BMVC 2017 (oral) revised versio

Deep View-Sensitive Pedestrian Attribute Inference in an end-to-end Model

Pedestrian attribute inference is a demanding problem in visual surveillance that can facilitate person retrieval, search and indexing. To exploit semantic relations between attributes, recent research treats it as a multi-label image classification task. The visual cues hinting at attributes can be strongly localized and inference of person attributes such as hair, backpack, shorts, etc., are highly dependent on the acquired view of the pedestrian. In this paper we assert this dependence in an end-to-end learning framework and show that a view-sensitive attribute inference is able to learn better attribute predictions. Our proposed model jointly predicts the coarse pose (view) of the pedestrian and learns specialized view-specific multi-label attribute predictions. We show in an extensive evaluation on three challenging datasets (PETA, RAP and WIDER) that our proposed end-to-end view-aware attribute prediction model provides competitive performance and improves on the published state-of-the-art on these datasets.Comment: accepted BMVC 201

Quantitative measures of corrosion and prevention: application to corrosion in agriculture

The corrosion protection factor (c.p.f.) and the corrosion condition (c.c.) are simple instruments for the study and evaluation of the contribution and efficiency of several methods of corrosion prevention and control. The application of c.p.f. and c.c. to corrosion and prevention in agriculture in The Netherlands is considered in detail. Attention is paid to relations between c.p.f. and c.c., the corrosion costs, possible cost savings and the applied corrosion protection scheme on farms. It is shown that the c.p.f. and the c.c. are useful expedients in a preliminary analysis of corrosion costs and possible cost savings on farms in relation to the corrosion protection methods applied.\ud \ud It is concluded that significant cost savings on arable farms can be derived by improving corrosion protection. No statistically significant cost savings are possible by improving corrosion protection on the dairy farms considered in this research