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COMPUTERS & GEOSCIENCES 2016年第Nov.期96卷 202-207页

作者： Farah, Mohamed Nefzi, Hafedh Farah, Imed Riadh RIADI Natl Sch Comp Sci Manouba Tunisia

Building remote sensing (RS) ontologies can undoubtedly help automatic interpretation of RS images Content. Ontology alignment is proven to be an effective ontology building process that enables reusing already existing semantic resources. The quality of the ontology alignment output highly depends on the similarity measures that have been considered as well as the way they are combined together. In the literature, research on similarity measures mainly focuses on how to build new or refine already existing similarity measures leading to a wide range of measures. However, few research addresses their dependencies and combination in order to evaluate the overall similarity of the concepts to be compared. In this paper, we first show how to select a reduced set of similarity measures to be used in the alignment process. Afterwards, we present a ranking model that allows sorting mappings between concepts coming from two different ontologies in a decreasing order of global similarity score. First experimentation shows that the proposed approach is promising. (C) 2016 Elsevier Ltd. All rights reserved.

关键词： Remote sensing Ontology conceptualization Ontology alignment Similarity measures Dimensionality reduction ordinal regression model

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Treatment selections using risk-benefit profiles based on data from comparative randomized clinical trials with multiple endpoints

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BIOSTATISTICS 2015年第1期16卷 60-72页

作者： Claggett, Brian Tian, Lu Castagno, Davide Wei, Lee-Jen Harvard Univ Sch Med Div Cardiovasc Med Boston MA 02115 USA Stanford Univ Sch Med Dept Hlth Res & Policy Stanford CA 94305 USA Univ Turin Div Cardiol Dept Med Sci I-10124 Turin Italy Harvard Univ Dept Biostat Boston MA 02115 USA

In a typical randomized clinical study to compare a new treatment with a control, oftentimes each study subject may experience any of several distinct outcomes during the study period, which collectively define the "risk-benefit" profile. To assess the effect of treatment, it is desirable to utilize the entirety of such outcome information. The times to these events, however, may not be observed completely due to, for example, competing risks or administrative censoring. The standard analyses based on the time to the first event, or individual component analyses with respect to each event time, are not ideal. In this paper, we classify each patient's risk-benefit profile, by considering all event times during follow-up, into several clinically meaningful ordinal categories. We first show how to make inferences for the treatment difference in a two-sample setting where categorical data are incomplete due to censoring. We then present a systematic procedure to identify patients who would benefit from a specific treatment using baseline covariate information. To obtain a valid and efficient system for personalized medicine, we utilize a cross-validation method for model building and evaluation and then make inferences using the final selected prediction procedure with an independent data set. The proposal is illustrated with the data from a clinical trial to evaluate a beta-blocker for treating chronic heart failure patients.

关键词： ordinal regression model Personalized medicine Subgroup analysis Survival analysis

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Improving ordinal regression model for Rating Inference through Optimizing Training Sample Selection

Improving Ordinal Regression Model for Rating Inference thro...

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第六届全国信息检索学术会议

作者：王会珍朱靖波 Key Laboratory of Medical Image Computing(Northeastern University) Ministry of Education Natural Language Processing Laboratory Northeastern University

This paper addresses the content-based rating inference issue in sentiment analysis,in which the user's assessments on social issues or products are determined with respect to multi-point scale instead of polarity (positive or negative).The most common way to tackle rating inference problem is to utilize machine learning algorithms such as ordinal regression *** practice,different reviews on the same object are generally provided by different users,and the rating annotations provided by different users are often not *** such cases,standard ordinal regression algorithms would fail due to the inconsistent rating annotation problem. To address this challenge,this paper proposes two approaches to improving standard ordinal regression algorithms by optimizing sample selection for training,including tolerance-based selection and ranking-loss-based selection *** on two publicly available English and Chinese restaurant review datasets demonstrated significant improvements over standard algorithms.

关键词： ordinal regression model Rating Inference Sentiment Analysis

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Healthy-unhealthy weight and time preference. Is there an association? An analysis through a consumer survey

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APPETITE 2014年 83卷 135-143页

作者： Cavaliere, Alessia De Marchi, Elisa Banterle, Alessandro Univ Milan Dept Econ Management & Quantitat Methods DEMM I-20133 Milan Italy

Individual time preference has been recognized as key driver in explaining consumers' probability to have a healthy weight or to incur excess weight problems. The term time preference refers to the rate at which a person is disposed to trade a current satisfaction for a future benefit. This characteristic may affect the extent at which individuals invest in health and may influence diet choices. The purpose of this paper is to analyse which could be the role of time preference (measured in terms of diet-related behaviours) in explaining consumers' healthy or unhealthy body weight. The analysis also considers other drivers predicted to influence BMI, specifically information searching, health-related activities and sociodemographic conditions. The survey was based on face-to-face interviews on a sample of 240 consumers living in Milan. In order to test the hypothesis, we performed a set of seven ORM regressions, all having consumers' BMI as the dependent variable. Each ORM contains a different block of explanatory variables, while time preference is always included among the regressors. The results suggest that the healthy weight condition is associated with a high orientation to the future, with a high interest in nutrition claims, a low attention to health-related claims, and a high level of education. On the opposite, the probability to be overweight or obese increases when consumers are less future-concerned and is associated with a low searching for nutrition claims and to a high interest in health claims. (C) 2014 Elsevier Ltd. All rights reserved.

关键词： Time preference Economics of obesity Consumer ordinal regression model

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An approach to the relationship between the information about Spanish financial entities' social responsibility with respect to customers and their own corporate reputation

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CUADERNOS DE ECONOMIA Y DIRECCION DE LA EMPRESA 2012年第3期15卷 130-140页

作者： de los Rios Berjillos, Araceli Ruiz Lozano, Mercedes Tirado Valencia, Pilar Carbonero Ruz, Mariano Univ Cordoba ETEA Fac Ciencias Econ & Empresariales Cordoba Spain

This paper presents an explanatory regression model contrasting the existence of a direct relationship between the information concerning the social responsibility of Spanish financial entities with respect to their customers and their corporate reputation. The dependent variable of the model is an ordinal variable defined by its position in the MERCO ranking of the entities under study;the ordinal regression model is the methodology applied. The model we present constitutes a first approach to ascertain the importance that information on social responsibility has in managing corporate reputation. This aspect of management will gradually become just as important as knowledge or added value management is today. The results obtained show a direct relationship between the category of corporate reputation and the information about innovation and risk management communicated by the financial entities. (C) 2012 ACEDE. Published by Elsevier Espana, S.L. All rights reserved.

关键词： Social responsibility Customers Corporate reputation ordinal regression model

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An analysis of farmers' perception of the new cooperative medical system in Liaoning Province, China

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BMC HEALTH SERVICES RESEARCH 2009年第1期9卷 1-8页

作者： Pan, Xilong Zhang, Ying Xu, Li Huang, Ju Zhao, Qianqian Peking Univ Hlth Sci Ctr Sch Publ Hlth Dept Hlth Policy & Management Beijing 100191 Peoples R China

Background: Since 2003, the number of pilot areas of the New Rural Cooperative Medical System (NRCMS) has increased in rural China. And the major efforts have been concentrated on the enrollment of prospective members. In this study, we examined the satisfaction of the rural residents with the NRCMS as well as factors affecting their attitudes towards the NRCMS. Methods: The data for this study were collected from a survey involving twenty counties in Liaoning Province. Interviews and focus groups were conducted between 10(th) January and 20(th) August 2008. A total of 2,780 people aged 18-72 were randomly selected and interviewed. Data were evaluated by nonparametric tests and ordinal regression models. Results: 71.6% of the study subjects were satisfied with the NRCMS. Single factor analysis showed that attitudes towards the NRCMS were influenced by gender, age, marital status, and self-rated health status. In the ordinal regression analysis, gender, age, and self-rated health status affect satisfaction ( P < 0.05). Conclusions: We found that a considerable proportion of farmers were satisfied with the NRCMS. Gender, age, and self-rated health status had significant effects on farmers' attitudes towards the NRCMS. The Chinese Central Government attempted to adopt active measures in the future to continuously improve the NRCMS, including initiating educational programs, building new medical facilities and increasing financial investment.

关键词： Liaoning Province ordinal regression model ordinal regression Analysis Administrative Village Peasant Worker

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Design of a bio-mathematical prediction model using serum tumor markers and immunohistochemistry in peritoneal carcinomatosis with ovarian involvement: a pilot study

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INTERNATIONAL JOURNAL OF GYNECOLOGICAL CANCER 2007年第6期17卷 1258-1263页

作者： Khatami, Z. Cross, P. Stevenson, M. R. Naik, R. BHR Hosp NHS Trust Dept Biochem London England Royal Grp Hosp Clin Res Support Ctr Belfast Antrim North Ireland Queen Elizabeth Hosp NHS Fdn Trust No Gynaecol Oncol Ctr Gateshead England Queen Elizabeth Hosp NHS Fdn Trust Dept Pathol Gateshead Hlth Gateshead NE9 6SX England

The diagnosis of disseminated intra-abdominal malignancy in women with ovarian involvement can be problematic. Whilst both blood tumor markers and use of immunohistochemical staining on tissue can help decide the origin of the tumor, this is done separately. This study looked at the blood and tissue marker profiles of 198 cases of disseminated malignancy to construct a model, which may help to determine tumor origin. The original histology material from 198 cases of disseminated intra-abdominal epithelial malignancy were reviewed, blind, and reassessed as to the likely site of origin. These cases had immunohistochemical (IHC) staining for cytokeratins (CK) 7 and 20, carcinoembryonic antigen (CEA) and CA125. Blood values for CEA and CA125 were also known at diagnosis. The histologic types of the tumors in this pilot study were of ovarian type morphologically in 130 cases (65.7%), nonovarian in 32 (16.1%), and not assigned in 36 cases (18.2%). The majority of the nonovarian cases were of mucinous type or too poorly differentiated to classify. Analysis showed an overall sensitivity and specificity of 93% and 69%, and positive predictive and negative predictive value of 92% and 71%, respectively, for a diagnosis of ovarian vs nonovarian origin using histology alone vs histology and IHC. Use of an ordinal regression developed a model which uses tissue staining for CK 7 and CEA along with blood levels of CEA to help determine the site of tumor origin.

关键词： histologic diagnosis immunohistochemistry ordinal regression model ovarian cancer tumor markers

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Computing marginal likelihoods from a single MCMC output

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STATISTICA NEERLANDICA 2005年第1期59卷 16-29页

作者： Chen, MH Univ Connecticut Dept Stat Storrs CT 06269 USA

In this article, we propose new Monte Carlo methods for computing a single marginal likelihood or several marginal likelihoods for the purpose of Bayesian model comparisons. The methods are motivated by Bayesian variable selection, in which the marginal likelihoods for all subset variable models are required to compute. The proposed estimates use only a single Markov chain Monte Carlo (MCMC) output from the joint posterior distribution and it does not require the specific structure or the form of the MCMC sampling algorithm that is used to generate the MCMC sample to be known. The theoretical properties of the proposed method are examined in detail. The applicability and usefulness of the proposed method are demonstrated via ordinal data probit regression models. A real dataset involving ordinal outcomes is used to further illustrate the proposed methodology.

关键词： Bayes factor cure rate survival model estimation of normalizing constant latent variable models ordinal regression model

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Evaluating the ecological sustainability of Austrian agricultural landscapes - the SINUS approach

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LAND USE POLICY 2004年第3期21卷 307-320页

作者： Peterseil, J Wrbka, T Plutzar, C Schmitzberger, I Kiss, A Szerencsits, E Reiter, K Schneider, W Suppan, F Beissmann, H Univ Vienna Inst Ecol & Conservat Biol Dept Conservat Biol A-1090 Vienna Austria Swiss Fed Res Stn Agroecol & Agr CH-8046 Zurich Switzerland Univ Nat Resources & Appl Life Sci Inst Surveying Remote Sensing & Land Informat A-1190 Vienna Austria Austrian Acad Sci Konrad Lorenz Inst Comparat Ethol A-1160 Vienna Austria

Sustainability has become a central term in environmental planning and policy since the late 1980s. However, an understanding of landscapes in terms of sustainability is still poorly developed. The project Spatial Indices for Land Use Sustainability was conducted to elaborate spatially explicit indicators for mapping ecological sustainability. Ecological sustainability was defined in terms of naturalness and biodiversity. The concept of hemeroby, a measure for the naturalness or conversely the human influence on ecosystems, was used for the assessment of ecological sustainability. Because direct information on the hemerobiotic state of landscapes was missing, shortcuts were analysed. An interdisciplinary multi-scale approach was developed that combined remote sensing data and a landscape ecological field survey, using a landscape typology as spatial reference frame. Variables describing the configuration and shape of the land mosaic were derived form a land cover classification based on landform and landscape fragmentation data. Two different assessment approaches were compared, (a) an expert-knowledge based fuzzy-rule system, and (b) an assessment based on the deviation of a certain landscape type from the mean hemerobiotic state. The hemeroby model was formulated and applied using ordinal regression techniques. The project results support the `pattern and process paradigm'. Variables describing landscape patterns turned out to be crucial for the model and were a good predictor for land-use intensity estimated by the hemerobiotic state. Based on each of the two approaches a `sustainability map' of Austria's cultural landscapes was derived. Despite the methodological differences of the two approaches similarities in the results could be demonstrated. Landscape-structure indicators were shown to be good indicators of ecological sustainability because they are related to ecological characteristics of landscapes such as naturalness and biodiversity. (C) 2003 Elsevier L

关键词： ecological sustainability landscape assessment landscape structure land cover scale hemerobiotic state ordinal regression model fuzzy set theory

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