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IEEE International Conference on Robotics and Automation

作者： Huang, Yi-Ting Cheng, Fan-Tien Hung, Min-Hsiung National Science Council of China Taiwan

ISBN: (纸本)9781424427888

In current semiconductor and TFT-LCD factories, periodic sampling is commonly adopted to monitor the stability of manufacturing processes and the quality of products (or workpieces). As for those non-sampled workpieces, their quality is usually monitored by such as a fault-detection-and-classification (FDC) server. However, this method may fail to detect defected products. For example, a workpiece with all the individual manufacturing process parameters being in-spec may still result in out-of-spec product quality. Under this circumstance, unless this certain defected workpiece is selected for sampling by chance, it cannot be detected by simply monitoring the manufacturing process parameters collected from the production equipment To solve the abovemendoned problem, this research proposes a product quality fault detection scheme (FDS), which utilizes the classification and regression tree to implement a model for identifying the relationship between process parameters and out-of-spec products. Through this model, each set of normal manufacturing process parameters can be real-time and on-line examined to detect failure or defected products.

关键词： Fault Detection Scheme classification and regression tree Virtual Metrology

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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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Toward Non-security Failures as a Predictor of Security Faults and Failures

Toward Non-security Failures as a Predictor of Security Faul...

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1st International Symposium on Engineering Secure Software and Systems

作者： Gegick, Michael Rotella, Pete Williams, Laurie North Carolina State Univ Dept Comp Sci 890 Oval Dr Raleigh NC 27695 USA Cisco Syst Inc Res Triangle Pk NC USA

ISBN: (纸本)9783642001987

In the search for metrics that can predict the presence of vulnerabilities early in the software life cycle, there may be some benefit to choosing metrics from the non-security realm. We analyzed non-security and security failure data reported for the year 2007 of a Cisco software system. We used non-security failure reports as input variables into a classification and regression tree (CART) model to determine the probability that a component will have at least one vulnerability. Using CART, we ranked all of the system components in descending order of their probabilities and found that 57% of the vulnerable components were in the top nine percent of the total component ranking, but with a 48% false positive rate. The results indicate that non-security failures can be used as one of the input variables for security-related prediction models.

关键词： Attack-prone classification and regression tree

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QSRR study of psychiatric drugs using classification and regression trees combined with adaptive Neuro-Fuzzy Inference System

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QSAR & COMBINATORIAL SCIENCE 2008年第6期27卷 729-739页

作者： Jalali-Heravi, Mehdi Shahbazikhah, Parvis Ghadiri-Bidhendi, Atieh Sharif Univ Technol Dept Chem Tehran Iran

A new Quantitative Structure-Retention Relationship (QSRR) approach was carried out for prediction of gas-liquid retention times of 124 psychiatric drugs in whole blood on fused-silica capillary column coated with crosslinked methylsilicone with nitrogen-phosphorus detection. After screening the descriptors, a total of 699 topological, geometric, and electronic descriptors (zero- to three-dimensional) representing various structural characteristics were calculated for each molecule in the dataset. Combined method of classification and regression tree (CART) as a feature selection method for the extraction of four relevant descriptors and Adaptive Neuro-Fuzzy Inference System (ANFIS) as a modeling technique was used for the prediction of retention times of diverse set of psychiatric drugs. The Root Mean Square Errors (RMSEs) for the calibration and prediction sets are 0.457 and 0.514, respectively.

关键词： adaptive neuro-fuzzy inference system classification and regression tree gas chromatography psychiatric drugs quantitative structure-retention relationship

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Variable precision neighborhood rough set based feature selection for credit scoring

Variable precision neighborhood rough set based feature sele...

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2009 International Conference on Computational Intelligence and Natural Computing(CINC 2009)

作者： Ping YAO College of Economics & Management,Heilongjiang Institute of Science and Technology Harbin,150027,China

As the credit industry has been growing rapidly,huge number of consumers’ credit data are collected by the credit department of the bank and credit scoring has become a very important issue. Usually,a large amount of redundant information and features are involved in the credit dataset,which leads to lower accuracy and higher complexity of the credit scoring model,so,effective feature selection methods are necessary for credit dataset with huge number of features. In this paper,a novel approach to credit scoring feature selection based on f variable precision neighborhood rough set is proposed. Three UCI credit datasets are selected to demonstrate the competitive performance of the presented method comparing with some other methods. Experiments show the proposed method get a better performance comparing with the classical rough set approaches.

关键词： variale precision neighorhood rough set feature selection credit scoring fuzzy entropy support vector machine classification and regression tree

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Evaluation of Environmental and Personal Susceptibility Characteristics That Modify Genetic Risks

Evaluation of Environmental and Personal Susceptibility Char...

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作者： Jing Shen

Study design in understanding gene—environment interaction plays a crucial role. Different study designs with their advantages and limitations are described in this chapter. Gene penetrance has been studied in several cancers, including breast and prostate cancer. Compared with high-penetrance genes, such as breast cancer (BRCA)1, BRCA2 in breast cancer, gene—environment interaction plays a major role in cancer development where low-penetrance genes are the major players. Genetic polymorphism is determined in low- and high-penetrance genes to identify cancer-related polymorphisms. The role of genetic and epigenetic factors in cancer development is discussed. Preventive approaches, especially in the epigenetic field, show promise. A discussion about different epidemiological methods with examples is provided. less

关键词： epigenetics genetic risk Biomarker classification and regression tree environmental factors high-penetrance genes personal susceptibility

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Feature selection based on SVM for credit scoring

Feature selection based on SVM for credit scoring

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2009 International Conference on Computational Intelligence and Natural Computing(CINC 2009)

作者： Ping Yao School of Economics & Management,Heilongjiang Institute of Science and Technology Harbin,150027,China

As the credit industry has been growing rapidly,huge number of consumers’ credit data are collected by the credit department of the bank and credit scoring has become a very important issue. Usually,a large amount of redundant information and features are involved in the credit dataset,which leads to lower accuracy and higher complexity of the credit scoring model,so,effective feature selection methods are necessary for credit dataset with huge number of features. This paper aims at comparing seven well-known feature selection methods for credit scoring. Which are t-test,principle component analysis (PCA),factor analysis (FA),stepwise regression,Rough Set (RS),classification and regression tree (CART) and Multivariate adaptive regression splines (MARS). Support vector machine (SVM) is used as the classification model. Two credit scoring databases are used in order to provide a reliable conclusion. Regarding the experimental results,the CART and MARS methods outperform the other methods by the overall accuracy and type I error and type II error.

关键词： credit scoring feature selection support vector machine t-test principle component analysis factor analysis stepwise regression rough set classification and regression tree multivariate adaptive regression splines

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The relative importance of factors affecting age-specific seedling survival of two co-occurring oak species in southern California

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FOREST ECOLOGY AND MANAGEMENT 2008年第7期255卷 3063-3074页

作者： Tyler, Claudia M. Davis, Frank W. Mahall, Bruce E. Univ Calif Santa Barbara Inst Computat Earth Syst Sci Santa Barbara CA 93106 USA

As has been reported in other oak habitats throughout the Northern Hemisphere, natural recruitment rates of young oaks in California are very low for some species and in some regions. The majority of experimental studies that contribute to our understanding of this oak recruitment pattern in California have been relatively short-term, conducted on a small-scale. Thus, while we have valuable information about the array of factors that are able to limit seedling establishment, we know much less about their relative importance, and how they vary across years, sites, or age classes. To investigate the impacts of factors limiting seedling and sapling establishment of valley oak (Quercus lobata) and coast live oak (Quercus agrifolia) across a landscape and over time we replicated experiments in four different years in Santa Barbara County, California. Experimental factors manipulated included cattle grazing, and access by deer and small mammals. Plots were distributed across three sites with a cumulative area of 200-ha, and seedling survival was recorded for a minimum of 5 years. We used classification and regression tree analysis (CART) to examine the relative importance of factors influencing survival of planted oaks at different life stages. The relative importance of limiting factors varied among age classes for both species. For initial seedling emergence and survival to 6 months planting year was the most important factor and rodent access was the second most important factor for both Q. lobata and Q. agrifolia. For survival of seedlings through their 1st year rodents, planting year, and site were major limiting factors, though their relative importance varied between the two species. For survival from 1.5 to 5 years, Q. lobata was strongly affected by rodents and site, while Q. agrifolia was mainly affected by site, deer browsing (which reduced survival), and indirect effects of winter-spring cattle grazing (which improved survival). Contrary to our expectations, b

关键词： Quercus lobata Quercus agrifolia Mediterranean CART classification and regression tree inter-annual variation seed predation herbivory browsing limiting factors oak recruitment

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Objective Pathological Voice Quality Assessment Based on HOS Features

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IEICE TRANSACTIONS ON INFORMATION AND SYSTEMS 2008年第12期E91D卷 2888-2891页

作者： Lee, Ji-Yeoun Jeong, Sangbae Choi, Hong-Shik Hahn, Minsoo Informat & Commun Univ Taejon 305732 South Korea Yonsei Univ Coll Med Yongdong Severance Hosp Dept OtorhinolaryngolInst Logoped & Phoniatr Seoul South Korea

This work proposes new features to improve the pathological voice quality classification performance. They are the means. the variances, and the perturbations of the higher-order statistics (HOS) such as the skewness and the kurtosis. The HOS-based features show meaningful differences among normal. grade 1, grade 2, and grade 3 voices, classified in the GRBAS scale. The jitter. the shimmer, the harmonic-to-noise ratio (HNR). and the variance of the short-time energy are utilized as the conventional features. The performances are measured by the classification and regression tree (CART) method. Specifically, the CART-based method by utilizing both the conventional features and the HOS-based ones shows its effectiveness in the pathological voice quality Measurement. with the classification accuracy of 87.8%.

关键词： pathological voice quality assessment higher-order statistics classification and regression tree GRBAS

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Industrial and occupational ergonomics in the petrochemical process industry: A regression trees approach

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ACCIDENT ANALYSIS AND PREVENTION 2008年第4期40卷 1468-1479页

作者： Bevilacqua, M. Ciarapica, F. E. Giacchetta, G. Univ Politecn Marche Dipartimento Energet I-60100 Ancona Italy Univ Bologna DIEM Forli I-47100 Forli Italy

This work is an attempt to apply classification tree methods to data regarding accidents in a medium-sized refinery, so as to identify the important relationships between the variables, which can be considered as decision-making rules when adopting any measures for improvement. The results obtained using the CART (classification and regression trees) method proved to be the most precise and, in general, they are encouraging concerning the use of tree diagrams as preliminary explorative techniques for the assessment of the ergonomic, management and operational parameters which influence high accident risk situations. The Occupational Injury analysis carried out in this paper was planned as a dynamic process and can be repeated systematically. The CART technique, which considers a very wide set of objective and predictive variables, shows new cause-effect correlations in occupational safety which had never been previously described, highlighting possible injury risk groups and supporting decision-making in these areas. The use of classification trees must not, however, be seen as an attempt to supplant other techniques, but as a complementary method which can be integrated into traditional types of analysis. (c) 2008 Elsevier Ltd. All rights reserved.

关键词： occupational injury ergonomics safety management data mining classification and regression tree rules induction

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