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Comparison of Neurocognition and Social Cognition between Schizoaffective Disorder, Mood Disorders, and Schizophrenia

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SOCIAL WORK RESEARCH 2017年第3期41卷 169-179页

作者： Xiao, Rong Bartel, Roxanne L. Brekke, John Univ Utah Dept Psychiat 501 Chipeta Way Salt Lake City UT 84108 USA Univ Southern Calif Suzanne Dworak Peck Sch Social Work Social Work Res Los Angeles CA USA

The relationship between schizoaffective disorder (SA), schizophrenia (SZ), and mood disorders (MD) is not well understood. Evaluating and comparing cognitive impairment in these disorders can help clarify how these disorders are related. The study's 47 participants with SZ, 94 with SA, and 39 with MD were evaluated with the Measurement and Treatment Research to Improve Cognition in Schizophrenia Consensus Cognitive Battery. Multivariate analysis of covariance and three Hotelling's T2 tests were used to compare cognition between participants with SZ, SA, and MD. A data mining classification tree determined the order of importance for all the significant predictors. Power analyses confirmed there was adequate statistical power. Results indicated that there was no difference between the performance of participants with SA and MD in all cognitive domains tested. Second, participants with SZ had worse performance than those with SA and MD in the cognitive domains of processing speed, working memory, visual learning, reasoning and problem solving, and social cognition, after controlling for gender, age, years of education, and substance use disorder. Finally, social cognition was the most important variable to distinguish SZ from the other two diagnoses, indicating that SA is more cognitively similar to MD than to SZ.

关键词： classification and regression tree data mining multivariate analysis of covariance severe mental illness

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Rule Extraction from DEWNN to Solve classification and regression Problems

Rule Extraction from DEWNN to Solve Classification and Regre...

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3rd International Conference on Swarm, Evolutionary and Memetic Computing (SEMCCO)

作者： Naveen, Nekuri Ravi, Vadlamani Rao, Chillarige Raghavendra Inst Dev & Res banking Technol Castle Hills 1 Hyderabad 500057 Andhra Pradesh India Univ Hyderabad Dept Comp & Informat Sci Hyderabad 500046 Andhra Pradesh India

ISBN: (纸本)9783642353796;9783642353802

This paper proposes a method to extract rules from differential evolution trained wavelet neural network (DEWNN) [1]. for solving classification and regression problems. The rule generation methods viz., Decision tree (DT), Ripper and classification and regression tree (CART) and Dynamic Evolving Neuro Fuzzy Inference System (DENFIS) are employed to extract rules from DEWNN for classification and regression problems respectively. The feature selection algorithm adapted by Chauhan et al., [1] is used in the present study. The effectiveness of the proposed hybrid is evaluated on Iris, Wine and four bankruptcy prediction datasets namely Spanish banks, Turkish banks, US banks, UK banks and Auto MPG dataset, Body fat dataset, Boston Housing dataset, Forest Fires dataset, Pollution dataset, by using 10-fold cross validation. From the results, it is concluded that the proposed hybrid method performed well in terms of sensitivity in classification problems.

关键词： Differential Evolution Trained Wavelet Neural Network classification regression Rule Extraction Decision tree Ripper classification and regression tree Dynamic Evolving Neuro Fuzzy Inference System

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Use of a Big Data Mining Technique to Extract Relative Importance of Performance Shaping Factors from Event Investigation Reports

Use of a Big Data Mining Technique to Extract Relative Impor...

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AHFE International Conference on Human Error, Reliability, Resilience, and Performance

作者： Park, Jinkyun Kim, Yochan Jung, Wondea Korea Atom Energy Res Inst Risk & Environm Safety Res Div 989-111 Daedeok Daero Daejeon 34057 South Korea

ISBN: (纸本)9783319606453;9783319606446

In this study, the relative importance of significant performance shaping factors (PSFs), which is critical for estimating the human error probability (HEP) of a given task environment is extracted from event investigation reports of domestic nuclear power plants (NPPs). Each event was caused by one or more human performance related problems (i.e., human errors), and its investigation report includes detailed information describing why the corresponding event has occurred. Based on 10 event reports, 47,220 data records were identified, which represent the task environment of 11 human errors in terms of significant PSFs. After that, the relative importance of the associated PSFs was analyzed by using a CART (classification and regression tree) method that is one of the representative techniques to scrutinize the characteristics of big data.

关键词： Human reliability analysis Performance shaping factors Event investigation report classification and regression tree Nuclear power plant

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A hybrid Decision Support System for the Risk Assessment of retinopathy development as a long term complication of Type 1 Diabetes Mellitus

A hybrid Decision Support System for the Risk Assessment of ...

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32nd Annual International Conference of the IEEE Engineering-in-Medicine-and-Biology-Society (EMBC 10)

作者： Skevofilakas, Marios Zarkogianni, Konstantia Karamanos, Basil G. Nikita, Konstantina S. Natl Tech Univ Athens Sch Elect & Comp Engn 9 Heroon Polytech Str GR-15780 Athens Greece Hippokrateion Hosp Diabetes Ctr GR-11527 Athens Greece

ISBN: (纸本)9781424441242

The aim of the present study is to design and develop a Decision Support System (DSS) closely coupled with an Electronic Medical Record (EMR), able to predict the risk of a Type 1 Diabetes Mellitus (T1DM) patient to develop retinopathy. The proposed system is able to store a wealth of information regarding the clinical state of the T1DM patient and continuously provide the health experts with predictions regarding the possible future complications that he may present. The DSS is a hybrid infrastructure combining a Feedforward Neural Network (FNN), a classification and regression tree (CART) and a Rule Induction C5.0 classifier, with an improved Hybrid Wavelet Neural Network (iHWNN). A voting mechanism is utilized to merge the results from the four classification models. The proposed DSS has been trained and evaluated using data from 55 T1DM patients, acquired by the Athens Hippokration Hospital in close collaboration with the EURODIAB research team. The DSS has shown an excellent performance resulting in an accuracy of 98%. Care has been taken to design and implement a consistent and continuously evolving Information Technology (IT) system by utilizing technologies such as smart agents periodically triggered to retrain the DSS with new cases added in the data repository.

关键词： CART DSS EMR EURODIAB FNN classification and regression tree decision support systems diseases electronic medical record eye feedforward neural nets feedforward neural network hybrid decision support system hybrid wavelet neural network iHWNN medical computing medical information systems retinopathy risk assessment type 1 diabetes mellitus wavelet transforms

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Variable Importance Scores

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Journal of Data Science 2021年第4期19卷 569-592页

作者： Loh, Wei-Yin Zhou, Peigen Department of Statistics University of Wisconsin 1300 University Avenue Madison 53706 WI United States

There are many methods of scoring the importance of variables in prediction of a response but not much is known about their accuracy. This paper partially fills the gap by introducing a new method based on the GUIDE algorithm and comparing it with 11 existing methods. For data without missing values, eight methods are shown to give biased scores that are too high or too low, depending on the type of variables (ordinal, binary or nominal) and whether or not they are dependent on other variables, even when all of them are independent of the response. Among the remaining four methods, only GUIDE continues to give unbiased scores if there are missing data values. It does this with a self-calibrating bias-correction step that is applicable to data with and without missing values. GUIDE also provides threshold scores for differentiating important from unimportant variables with 95 and 99 percent confidence. Correlations of the scores to the predictive power of the methods are studied in three real data sets. For many methods, correlations with marginal predictive power are much higher than with conditional predictive power. © 2021 Center for Applied Statistics, School of Statistics, Renmin University of China.

关键词： bias correction classification and regression tree missing values prediction

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Evidence on women trafficked for sexual exploitation: A rights based analysis

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EUROPEAN JOURNAL OF LAW AND ECONOMICS 2010年第1期29卷 15-42页

作者： Bettio, Francesca Nandi, Tushar K. Univ Siena Dept Econ I-53100 Siena Italy

The aim of this paper is to investigate which factors influence the pattern of enforcement (violation) of basic rights among women trafficked for sexual exploitation. A conceptual framework is adopted where the degree of agency and the possibility to influence the terms of sex-based transactions are seen as conditional on the enforcement of some basic rights. Using data collected by the International Organization for Migration (IOM) on women assisted by the organization after having been trafficked for sexual exploitation, we investigate the enforcement (violation) of five uncompromisable rights, namely the right to physical integrity, to move freely, to have access to medical care, to use condoms, and to exercise choice over sexual services. By combining classification tree analysis and ordered probit estimation we find that working location and country of work are the main determinants of rights enforcement, while individual and family characteristics play a marginal role. Specifically, we find that (1) in lower market segments working on the street is comparatively less 'at risk' of rights violation;(2) there is no consistently 'good' or 'bad' country of work, but public awareness on trafficking within the country is important;(3) the strength of organised crime in the country of work matters only in conjunction with other local factors, and (4) being trafficked within one's country, as opposed to being trafficked internationally, is associated with higher risk of rights violation.

关键词： Human trafficking Sexual exploitation Basic rights classification and regression tree Ordered probit

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Facial Expression Recognition using Decision trees 13

Facial Expression Recognition using Decision Trees

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13th International Conference on Computer Graphics, Imaging and Visualization (CGiV)

作者： Salmam, Fatima Zahra Madani, Abdellah Kissi, Mohamed Univ Chouaib Doukkali Fac Sci Dept Comp Sci Lab LAROSERI El Jadida Morocco Univ Chouaib Doukkali Fac Sci Dept Comp Sci Lab LIMA El Jadida Morocco

ISBN: (纸本)9781509008117

Emotion recognition from facial expressions is generally performed in three steps: face detection, features extraction and classification of expressions. The present work focuses on two points: Firstly, a new extraction method is presented based on the geometric approach. This method consists of calculating six distances in order to measure parts of the face that better describe a facial expression. Secondly, an automatic supervised learning method called decision tree is applied on two databases (JAFEE and COHEN), in order to have a facial expressions classifying system with seven possible classes (six basic emotions plus neutral);this system uses as input the six distances previously calculated (using Euclidian,Manhattan or Minkowski distance) for each face. Our results achieved a recognition rate of 89.20% and 90.61% respectively in JAFFE and COHEN database.

关键词： classification and regression tree Emotions recognition Facial expressions Facial landmarks Supervised Descent Method

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Modeling large scale geogenic contamination of groundwater, combination of geochemical expertise and statistical techniques 18

Modeling large scale geogenic contamination of groundwater, ...

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Combined IMACS World Congress/Modelling and Simulation Society-of-Australia-and-New-Zealand (MSSANZ)/18th Biennial Conference on Modelling and Simulation

作者： Amini, M. Johnson, A. Abbaspour, K. C. Mueller, K. Eawag Swiss Fed Inst Aquat Sci & Technol CH-8600 Dubendorf Switzerland

ISBN: (纸本)9780975840078

There is an increasing interest in modeling groundwater contamination, particularly geogenic contaminant, on a large scale both from the researcher's as well as policy maker's point of view. However, modeling large scale groundwater contamination is very challenging due to the incomplete understanding of geochemical and hydrological processes in the aquifer. Despite the incomplete understanding, existing knowledge provides sufficient hints to develop predictive models of geogenic contamination. In this study we used a global database of fluoride measurements (>60,000 entities), as well as global-scale information relevant to soil, geology, elevation, climate, and hydrology to evaluate several hybrid methods. The hybrid methods were developed by combining two classification techniques including classification tree (CART) and knowledge based clustering (KBC) and three predictive techniques including multiple linear regression (MLR), adoptive neuro-fuzzy inference system (ANFIS) and logistic regression (LR). The results indicated that combination of classification techniques and nonlinear predictive method (ANFIS and LR) were more reliable than others and provided a better prediction capability. Among the different hybrid procedures, combination of KBC-ANFIS and also CART - ANFIS resulted in larger sensitivities and smaller false negative rates for both training and test data sets. However, as the CART classifier is very unstable and very sensitive to re-sampling, the combination of KBC and ANFIS or LR is preferred

关键词： Knowledge Based Clustering classification and regression tree Adoptive Neuro Fuzzy Inference System Logistic regression Hybrid methods

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MIXED-VARIABLE METAMODELING METHODS FOR DESIGNING MULTI-MATERIAL STRUCTURES

MIXED-VARIABLE METAMODELING METHODS FOR DESIGNING MULTI-MATE...

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ASME International Design Engineering Technical Conference / Computer and Information in Engineering Conference (IDETC/CIE)

作者： Xu, Hongyi Chuang, Ching-Hung Yang, Ren-Jye Ford Motor Co Res & Adv Engn Dearborn MI 48121 USA

ISBN: (纸本)9780791850114

To establish metamodels for the multi-material structure design problems, the material selection of each component is considered as a categorical design variable. One challenging task is to establish an accurate mixed-variable metamodel. It is critical to reduce the prediction error of the mixed-variable metamodel in order to achieve a feasible design with superior performance in the metamodel-based optimization. This paper investigates two different strategies of mixed-variable metamodeling: "feature separating" strategy and "all-in-one" strategy. A supervised learning-aided method is proposed to improve the "feature separating" metamodels. The proposed method is compared with several existing mixed-variable metamodeling methods on three engineering benchmark problems to understand their relative merits. These methods include Neural Network (NN) regression, classification and regression tree (CART) and Gaussian Process (GP). A new Polynomial Coefficient Metric is developed to quantify the adequacy of training data. This study provides insight and guidance for establishing proper metamodels on multi-material structural design problems.

关键词： Multi-material Mixed-variable CoKriging Neural network classification and regression tree Gaussian Supervised learning Data adequacy

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Real-time Prediction of Information Search Channel Using Data Mining Techniques 1

Real-time Prediction of Information Search Channel Using Dat...

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International Conference on Green Computing and Internet of Things (ICGCIoT)

作者： Khatwani, Gaurav Srivastava, Praveen Ranjan Indian Inst Management IT Syst Rohtak Haryana India

ISBN: (纸本)9781467379106

One of the biggest challenges associated with using the Internet as a real-time marketing vehicle concerns digital media fragmentation. The vast amount of potential media sources and platforms that are available to marketers entails that it can be very difficult to formulate a succinct strategy through which they can interact with the existing and potential customers. While a large number of businesses typically use consumer's previous actions as a means of understanding their information search behavior, very little is understood about how demographics influence information search and the use of various digital platforms. Previous research has focused on identifying these demographic factors but, as yet, no one has developed a real-time model that is capable of predicting consumer's information search preferences. This research considers four information search channels: personal, marketer-dominated, neutral and experiential channels, and assesses the extent to which existing classification techniques, such as classification and regression tree, neural networks and support vector machines, can be effectively employed to forecast individual's search preferences according to their demographic context. It is envisaged that the development of a method that can accurately forecast search behavior will help organizations to ensure that they allocate marketing resources in an efficient and effective manner.

关键词： Neural network Support vector machine classification and regression tree Information search channels

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