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A comparative study of variable selection methods in the context of developing psychiatric screening instruments

STATISTICS IN MEDICINE 2014年第3期33卷 401-421页

作者： Lu, Feihan Petkova, Eva Columbia Univ Dept Stat New York NY 10027 USA NYU Dept Child & Adolescent Psychiat New York NY 10016 USA Nathan S Kline Inst Psychiat Res Orangeburg NY 10962 USA

The development of screening instruments for psychiatric disorders involves item selection from a pool of items in existing questionnaires assessing clinical and behavioral phenotypes. A screening instrument should consist of only a few items and have good accuracy in classifying cases and non-cases. Variable/item selection methods such as Least Absolute Shrinkage and Selection Operator (LASSO), Elastic Net, classification and regression tree, Random Forest, and the two-sample t-test can be used in such context. Unlike situations where variable selection methods are most commonly applied (e.g., ultra high-dimensional genetic or imaging data), psychiatric data usually have lower dimensions and are characterized by the following factors: correlations and possible interactions among predictors, unobservability of important variables (i.e., true variables not measured by available questionnaires), amount and pattern of missing values in the predictors, and prevalence of cases in the training data. We investigate how these factors affect the performance of several variable selection methods and compare them with respect to selection performance and prediction error rate via simulations. Our results demonstrated that: (1) for complete data, LASSO and Elastic Net outperformed other methods with respect to variable selection and future data prediction, and (2) for certain types of incomplete data, Random Forest induced bias in imputation, leading to incorrect ranking of variable importance. We propose the Imputed-LASSO combining Random Forest imputation and LASSO;this approach offsets the bias in Random Forest and offers a simple yet efficient item selection approach for missing data. As an illustration, we apply the methods to items from the standard Autism Diagnostic Interview-Revised version. Copyright (c) 2013 John Wiley & Sons, Ltd.

关键词： least absolute shrinkage and selection operator elastic net classification and regression tree random forest two-sample t-test missing data imputation

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Feature extraction for defect classification and yield enhancement in color filter and micro-lens manufacturing: An empirical study

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JOURNAL OF INDUSTRIAL AND PRODUCTION ENGINEERING 2013年第8期30卷 510-517页

作者： Chen, Ying-Jen Lin, Tzu-Hsiang Chang, Kuo-Hao Chien, Chen-Fu Natl Tsing Hua Univ Dept Ind Engn & Engn Management 101Sect 2Kuang Fu Rd Hsinchu 30013 Taiwan

Yield improvement is an important issue in semiconductor-manufacturing supply chains, including color filter and micro-lens manufacturing. In the color filter and micro-lens processes, it is critical to quickly identify the defect pattern through the defect pictures and then take corrective actions to avoid greater yield loss. Until now, defect pattern recognition heavily relies on domain experts' knowledge, which easily causes inconsistent classification results from person to person and unsatisfactory performance. In this study, a framework is proposed to achieve automatic defect detection and classification in color filter and micro-lens manufacturing to enhance the decision quality of pattern recognition. In particular, the proposed framework integrates Canny edge detection and classification and regression tree methodology. To validate the viability of the proposed framework in real settings, an empirical study was conducted in collaboration with a leading complementary metal oxide semiconductor image sensor foundry in Taiwan. The results not only showed the effectiveness of the proposed framework but also demonstrated the practical values.

关键词： surface defect detection color filter and micro-lens processes classification and regression tree feature extraction yield enhancement

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Identification of Atrial Fibrillation Using Complex Network Similarity

Identification of Atrial Fibrillation Using Complex Network ...

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The 7th International Conference on Management and Service Science(MASS 2013)

作者： Yajuan Zhang Yuanyuan Wang National Key Laboratory of ASIC and System Fudan University Department of Electronic Engineering Fudan University

We investigate the use of complex network similarity for the identification of atrial fibrillation. The similarity of the network is estimated via the joint recurrence plot and Hamming distance. Firstly, we transform multi-electrodes epicardium signals recorded from dogs into the recurrence complex network. Then, we extract features representing its similarity. Finally, epicardium signals are classified utilizing the lassification and regression tree with extracted *** method is validated using 1000 samples including 500 atrial fibrillation cases and 500 normal sinus ones. The sensitivity, specificity and accuracy of the identification are 98.2%, 98.8% and 98.5% respectively. This experiment indicates that our approach may lay a foundation for the prediction of the onset of atrial fibrillation.

关键词： Recurrence Complex Network Phase Space Reconstruction classification and regression tree Atrial Fibrillation

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Critical Predictors for the Early Detection of Conversion From Unipolar Major Depressive Disorder to Bipolar Disorder: Nationwide Population-Based Retrospective Cohort Study

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JMIR MEDICAL INFORMATICS 2020年第4期8卷 e14278页

作者： Hu, Ya-Han Chen, Kuanchin Chang, I-Chiu Shen, Cheng-Che Natl Chung Cheng Univ Ctr Innovat Res Aging Soc Chiayi Cty Taiwan Natl Cheng Kung Univ MOST AI Biomed Res Ctr Tainan Taiwan Natl Cent Univ Dept Informat Management Taoyuan Taiwan Western Michigan Univ Dept Business Informat Syst Kalamazoo MI 49008 USA Natl Chung Cheng Univ Dept Informat Management Chiayi Cty Taiwan Natl Chung Cheng Univ Inst Healthcare Informat Management Chiayi Cty Taiwan Taichung Vet Gen Hosp Dept Psychiat Chiayi Branch 600Sect 2Shixian Rd Chiayi 60090 Taiwan Natl Yang Ming Univ Sch Med Taipei Taiwan

Background: Unipolar major depressive disorder (MDD) and bipolar disorder are two major mood disorders. The two disorders have different treatment strategies and prognoses. However, bipolar disorder may begin with depression and could be diagnosed as MDD in the initial stage, which may later contribute to treatment failure. Previous studies indicated that a high proportion of patients diagnosed with MDD will develop bipolar disorder over time. This kind of hidden bipolar disorder may contribute to the treatment resistance observed in patients with MDD. Objective: In this population-based study, our aim was to investigate the rate and risk factors of a diagnostic change from unipolar MDD to bipolar disorder during a 10-year follow-up. Furthermore, a risk stratification model was developed for MDD-to-bipolar disorder conversion. Methods: We conducted a retrospective cohort study involving patients who were newly diagnosed with MDD between January 1, 2000, and December 31, 2004, by using the Taiwan National Health Insurance Research Database. All patients with depression were observed until (1) diagnosis of bipolar disorder by a psychiatrist, (2) death, or (3) December 31, 2013. All patients with depression were divided into the following two groups, according to whether bipolar disorder was diagnosed during the follow-up period: converted group and nonconverted group. Six groups of variables within the first 6 months of enrollment, including personal characteristics, physical comorbidities, psychiatric comorbidities, health care usage behaviors, disorder severity, and psychotropic use, were extracted and were included in a classification and regression tree (CART) analysis to generate a risk stratification model for MDD-to-bipolar disorder conversion. Results: Our study enrolled 2820 patients with MDD. During the follow-up period, 536 patients were diagnosed with bipolar disorder (conversion rate=19.0%). The CART method identified five variables (kinds of antipsychoti

关键词： major depressive disorder bipolar disorder National Health Insurance Database data mining 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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Real-time Prediction of Information Search Channel Using Data Mining Techniques

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

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International Conference on Green Computing and Internet of Things

作者： Gaurav Khatwani Praveen Ranjan Srivastava IT Systems Indian Institute of Management

ISBN: (纸本)9781467379113

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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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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Prediction of corporate credit ratings with machine learning: Simple interpretative models

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FINANCE RESEARCH LETTERS 2023年 58卷

作者： Galil, Koresh Hauptman, Ami Rosenboim, Rosit Levy Ben Gurion Univ Negev Dept Econ Beer Sheva Israel Ben Gurion Univ Negev Dept Econ POB 653 IL-8410501 Beer Sheva Israel Sapir Acad Coll Dept Comp Sci Sderot Israel Sapir Acad Coll Dept Appl Econ Sderot Israel Ben Gurion Univ Negev Econ Dept Beer Sheva Israel Sapir Coll Comp Sci Dept Sderot Israel Sapir Coll Appl Econ Dept Sderot Israel

This study utilizes machine learning techniques, notably classification and regression trees (CART) and support vector regression (SVR), to predict corporate credit ratings. While SVR marginally outperforms in accuracy, CART offers interpretability. However, unconstrained models can produce non-monotonic relationships between credit ratings and core features, an undesired outcome. To circumvent this, we recommend restricted CART models that ensure interpretable, theory-consistent results. We underscore the importance of company size in credit rating prediction with an ideal model integrating size, interest coverage, and dividends. Although being a large-cap company is crucial, it doesn't guarantee high ratings, and small-cap companies rarely secure investment-grade ratings.

关键词： Corporate ratings Machine learning classification and regression tree Support Vector regression CART SVR Size

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Severity modeling of work zone crashes in New Jersey using machine learning models

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JOURNAL OF TRANSPORTATION SAFETY & SECURITY 2023年第6期15卷 604-635页

作者： Hasan, Ahmed Sajid Kabir, Md Asif Bin Jalayer, Mohammad Das, Subasish Rowan Univ Civil & Environm Engn 201 Mullica Hill Rd Glassboro NJ 08028 USA United Int Univ Civil Engn Dhaka Bangladesh Texas A&M Univ Transportat Inst Coll Stn College Stn TX USA

In the United States, the probability of work zone crashes has increased due to an increase in renovation works by transportation infrastructures. The severity of work zone crashes is associated with multiple contributing factors such as the roadway's geometric design features, temporal variables, environmental conditions, types of vehicles, and driver behaviors. For this study, we acquired and analyzed three years (2016-2018) of work zone crash data from the state of New Jersey. We investigated the performance of several machine learning methods, including Support Vector Machine, Random Forest, Catboost, Light GBM, and XGBoost to predict the type of injury severity resulting from work zone crashes. To evaluate models' performances, some statistical evaluation parameters such as accuracy, precision, and recall scores were calculated. In addition, a sensitivity analysis was conducted to assess the impact of the most influential factors in work zone-related crashes. Random Forest and Catboost outperformed the other models in terms of predicting fatal, major, and minor injuries. According to the sensitivity analysis, crash type and speed limit were the most significantly associated variables with crash severity. The findings of this study are expected to facilitate the identification of appropriate countermeasures for reducing the severity of work zone crashes.

关键词： Support Vector Machine Random Forest classification and regression tree work zone crash sensitivity analysis New Jersey

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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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