Lessons in Biostatistics
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Analysis of Imbalance Strategies Recommendation Using a Meta-Learning Approach
7th ICML Workshop on Automated Machine Learning (2020) Analysis of Imbalance Strategies Recommendation using a Meta-Learning Approach Afonso Jos´eCosta [email protected] CISUC, Department of Informatics Engineering, University of Coimbra, Portugal Miriam Seoane Santos [email protected] CISUC, Department of Informatics Engineering, University of Coimbra, Portugal Carlos Soares [email protected] Fraunhofer AICOS and LIACC, Faculty of Engineering, University of Porto, Portugal Pedro Henriques Abreu [email protected] CISUC, Department of Informatics Engineering, University of Coimbra, Portugal Abstract Class imbalance is known to degrade the performance of classifiers. To address this is- sue, imbalance strategies, such as oversampling or undersampling, are often employed to balance the class distribution of datasets. However, although several strategies have been proposed throughout the years, there is no one-fits-all solution for imbalanced datasets. In this context, meta-learning arises a way to recommend proper strategies to handle class imbalance, based on data characteristics. Nonetheless, in related work, recommendation- based systems do not focus on how the recommendation process is conducted, thus lacking interpretable knowledge. In this paper, several meta-characteristics are identified in order to provide interpretable knowledge to guide the selection of imbalance strategies, using Ex- ceptional Preferences Mining. The experimental setup considers 163 real-world imbalanced datasets, preprocessed with 9 well-known resampling algorithms. Our findings elaborate on the identification of meta-characteristics that demonstrate when using preprocessing algo- rithms is advantageous for the problem and when maintaining the original dataset benefits classification performance. 1. Introduction Class imbalance is known to severely affect the data quality. -
Particle Filtering-Based Maximum Likelihood Estimation for Financial Parameter Estimation
View metadata, citation and similar papers at core.ac.uk brought to you by CORE provided by Spiral - Imperial College Digital Repository Particle Filtering-based Maximum Likelihood Estimation for Financial Parameter Estimation Jinzhe Yang Binghuan Lin Wayne Luk Terence Nahar Imperial College & SWIP Techila Technologies Ltd Imperial College SWIP London & Edinburgh, UK Tampere University of Technology London, UK London, UK [email protected] Tampere, Finland [email protected] [email protected] [email protected] Abstract—This paper presents a novel method for estimating result, which cannot meet runtime requirement in practice. In parameters of financial models with jump diffusions. It is a addition, high performance computing (HPC) technologies are Particle Filter based Maximum Likelihood Estimation process, still new to the finance industry. We provide a PF based MLE which uses particle streams to enable efficient evaluation of con- straints and weights. We also provide a CPU-FPGA collaborative solution to estimate parameters of jump diffusion models, and design for parameter estimation of Stochastic Volatility with we utilise HPC technology to speedup the estimation scheme Correlated and Contemporaneous Jumps model as a case study. so that it would be acceptable for practical use. The result is evaluated by comparing with a CPU and a cloud This paper presents the algorithm development, as well computing platform. We show 14 times speed up for the FPGA as the pipeline design solution in FPGA technology, of PF design compared with the CPU, and similar speedup but better convergence compared with an alternative parallelisation scheme based MLE for parameter estimation of Stochastic Volatility using Techila Middleware on a multi-CPU environment. -
Resampling Hypothesis Tests for Autocorrelated Fields
JANUARY 1997 WILKS 65 Resampling Hypothesis Tests for Autocorrelated Fields D. S. WILKS Department of Soil, Crop and Atmospheric Sciences, Cornell University, Ithaca, New York (Manuscript received 5 March 1996, in ®nal form 10 May 1996) ABSTRACT Presently employed hypothesis tests for multivariate geophysical data (e.g., climatic ®elds) require the as- sumption that either the data are serially uncorrelated, or spatially uncorrelated, or both. Good methods have been developed to deal with temporal correlation, but generalization of these methods to multivariate problems involving spatial correlation has been problematic, particularly when (as is often the case) sample sizes are small relative to the dimension of the data vectors. Spatial correlation has been handled successfully by resampling methods when the temporal correlation can be neglected, at least according to the null hypothesis. This paper describes the construction of resampling tests for differences of means that account simultaneously for temporal and spatial correlation. First, univariate tests are derived that respect temporal correlation in the data, using the relatively new concept of ``moving blocks'' bootstrap resampling. These tests perform accurately for small samples and are nearly as powerful as existing alternatives. Simultaneous application of these univariate resam- pling tests to elements of data vectors (or ®elds) yields a powerful (i.e., sensitive) multivariate test in which the cross correlation between elements of the data vectors is successfully captured by the resampling, rather than through explicit modeling and estimation. 1. Introduction dle portion of Fig. 1. First, standard tests, including the t test, rest on the assumption that the underlying data It is often of interest to compare two datasets for are composed of independent samples from their parent differences with respect to particular attributes. -
Receiver Operating Characteristic (ROC) Methods in Diagnostic Imaging
8/2/2017 Receiver Operating Characteristic (ROC) Methods in Diagnostic Imaging Elizabeth A. Krupinski, PhD Department Radiology & Imaging Sciences Emory University Bit of History • Developed early 1950s based on principles SDT for eval radar operators detecting enemy aircraft & missiles • Contributions from engineering, psychology & mathematics • Lee Lusted introduced medicine 1960s with significant effort on gaining better understanding decision-making • Result of radiology studies after WWII to determine which of 4 radiographic & fluoroscopic techniques better for TB screening • Goal = single imaging technique outperform others • Found intra & inter-observer variation so high impossible determine • Necessary to build systems generate better images so radiologists’ performance could improve (i.e., reduce observer variability) & develop methods evaluate these new systems & assess impact on observer performance Basics • ROC traditionally binary decision task – target/signal (e.g., lesion, disease, missile) present versus target/signal absent, or in case classification rather than detection target/signal belongs to class 1 (e.g., cancer, enemy) or class 2 (e.g., not cancer, friend) • ROC analysis these two conditions must be mutually exclusive 1 8/2/2017 2 x 2 Matrix Decision = Target Decision = Target Present Absent Truth = Target Present True Positive (TP) False Negative (FN) Truth = Target Absent False Positive (FP) True Negative (TN) Common Performance Metrics • Sensitivity = TP/(TP + FN) • Specificity = TN/(TN + FP) • Accuracy = (TP -
Sampling and Resampling
Sampling and Resampling Thomas Lumley Biostatistics 2005-11-3 Basic Problem (Frequentist) statistics is based on the sampling distribution of statistics: Given a statistic Tn and a true data distribution, what is the distribution of Tn • The mean of samples of size 10 from an Exponential • The median of samples of size 20 from a Cauchy • The difference in Pearson’s coefficient of skewness ((X¯ − m)/s) between two samples of size 40 from a Weibull(3,7). This is easy: you have written programs to do it. In 512-3 you learn how to do it analytically in some cases. But... We really want to know: What is the distribution of Tn in sampling from the true data distribution But we don’t know the true data distribution. Empirical distribution On the other hand, we do have an estimate of the true data distribution. It should look like the sample data distribution. (we write Fn for the sample data distribution and F for the true data distribution). The Glivenko–Cantelli theorem says that the cumulative distribution function of the sample converges uniformly to the true cumulative distribution. We can work out the sampling distribution of Tn(Fn) by simulation, and hope that this is close to that of Tn(F ). Simulating from Fn just involves taking a sample, with replace- ment, from the observed data. This is called the bootstrap. We ∗ write Fn for the data distribution of a resample. Too good to be true? There are obviously some limits to this • It requires large enough samples for Fn to be close to F . -
Kwame Nkrumah University of Science and Technology, Kumasi
KWAME NKRUMAH UNIVERSITY OF SCIENCE AND TECHNOLOGY, KUMASI, GHANA Assessing the Social Impacts of Illegal Gold Mining Activities at Dunkwa-On-Offin by Judith Selassie Garr (B.A, Social Science) A Thesis submitted to the Department of Building Technology, College of Art and Built Environment in partial fulfilment of the requirement for a degree of MASTER OF SCIENCE NOVEMBER, 2018 DECLARATION I hereby declare that this work is the result of my own original research and this thesis has neither in whole nor in part been prescribed by another degree elsewhere. References to other people’s work have been duly cited. STUDENT: JUDITH S. GARR (PG1150417) Signature: ........................................................... Date: .................................................................. Certified by SUPERVISOR: PROF. EDWARD BADU Signature: ........................................................... Date: ................................................................... Certified by THE HEAD OF DEPARTMENT: PROF. B. K. BAIDEN Signature: ........................................................... Date: ................................................................... i ABSTRACT Mining activities are undertaken in many parts of the world where mineral deposits are found. In developing nations such as Ghana, the activity is done both legally and illegally, often with very little or no supervision, hence much damage is done to the water bodies where the activities are carried out. This study sought to assess the social impacts of illegal gold mining activities at Dunkwa-On-Offin, the capital town of Upper Denkyira East Municipality in the Central Region of Ghana. The main objectives of the research are to identify factors that trigger illegal mining; to identify social effects of illegal gold mining activities on inhabitants of Dunkwa-on-Offin; and to suggest effective ways in curbing illegal mining activities. Based on the approach to data collection, this study adopts both the quantitative and qualitative approach. -
The Association Between Four Scoring Systems and 30-Day Mortality Among Intensive Care Patients with Sepsis
www.nature.com/scientificreports OPEN The association between four scoring systems and 30‑day mortality among intensive care patients with sepsis: a cohort study Tianyang Hu 1,4, Huajie Lv2,4 & Youfan Jiang3* Several commonly used scoring systems (SOFA, SAPS II, LODS, and SIRS) are currently lacking large sample data to confrm the predictive value of 30‑day mortality from sepsis, and their clinical net benefts of predicting mortality are still inconclusive. The baseline data, LODS score, SAPS II score, SIRS score, SOFA score, and 30‑day prognosis of patients who met the diagnostic criteria of sepsis were retrieved from the Medical Information Mart for Intensive Care III (MIMIC‑III) intensive care unit (ICU) database. Receiver operating characteristic (ROC) curves and comparisons between the areas under the ROC curves (AUC) were conducted. Decision curve analysis (DCA) was performed to determine the net benefts between the four scoring systems and 30‑day mortality of sepsis. For all cases in the cohort study, the AUC of LODS, SAPS II, SIRS, SOFA were 0.733, 0.787, 0.597, and 0.688, respectively. The diferences between the scoring systems were statistically signifcant (all P‑values < 0.0001), and stratifed analyses (the elderly and non‑elderly) also showed the superiority of SAPS II among the four systems. According to the DCA, the net beneft ranges in descending order were SAPS II, LODS, SOFA, and SIRS. For stratifed analyses of the elderly or non‑elderly groups, the results also showed that SAPS II had the most net beneft. Among the four commonly used scoring systems, the SAPS II score has the highest predictive value for 30‑day mortality from sepsis, which is better than LODS, SIRS, and SOFA. -
Investigating Data Management Practices in Australian Universities
Investigating Data Management Practices in Australian Universities Margaret Henty, The Australian National University Belinda Weaver, The University of Queensland Stephanie Bradbury, Queensland University of Technology Simon Porter, The University of Melbourne http://www.apsr.edu.au/investigating_data_management July, 2008 ii Table of Contents Introduction ...................................................................................... 1 About the survey ................................................................................ 1 About this report ................................................................................ 1 The respondents................................................................................. 2 The survey results............................................................................... 2 Tables and Comments .......................................................................... 3 Digital data .................................................................................... 3 Non-digital data forms....................................................................... 3 Types of digital data ......................................................................... 4 Size of data collection ....................................................................... 5 Software used for analysis or manipulation .............................................. 6 Software storage and retention ............................................................ 7 Research Data Management Plans......................................................... -
Quality Assurance
500A ANNUAL MEETING ABSTRACTS 2051 Synchronous Lung Adenocarcinoma and Primary Pulmonary MALT there are no established guidelines for storage methods of precut controls, patient tissue, Lymphoma: An Underdiagnosed Entity Associated with KRAS Mutations or microarrays (TMAs). We sought to determine loss of antigenicity and potential for J Yao, N Rehktman, K Nafa, A Dogan, M Ladanyi, ME Arcila. Memorial Sloan-Kettering preservation by refrigeration in a longitudinal prospective study. Cancer Center, New York, NY. Design: Selected diagnostic or prognostic antibodies included p53, IDH-1, Ki67, Background: Pulmonary extranodal marginal zone lymphoma (MZL / MALT) is a synaptophysin, and androgen receptor (AR). TMA with 22 cores from small cell rare entity accounting for less than 0.5% of primary pulmonary malignancies. The carcinomas, prostatic adenocarcinomas, and gliomas was constructed; 125 slides were occurrence of lung adenocarcinoma (AD) and primary pulmonary MALT lymphoma cut at 4 microns at time 0. Slides were stored exposed to air at room temperature (RT), as collision tumors have only been rarely reported. We investigated the concurrent 4C, or -20C; IHC was performed on the Leica Bond III at time 0, weeks 1, 2, 4, and 6. incidence of these two entities in a large cohort of lung AD cases submitted for routine Each tissue core was scored for overall intensity (0 to 3+) and % of cells staining (100 molecular diagnostic testing. cells within hotspot). Loss of antigenicity was defi ned as a decrease of staining intensity Design: Consecutive lung AD cases were reviewed and categorized based on the by one order and/or loss of≥10% of positive cells compared to time 0. -
Efficient Computation of Adjusted P-Values for Resampling-Based Stepdown Multiple Testing
University of Zurich Department of Economics Working Paper Series ISSN 1664-7041 (print) ISSN 1664-705X (online) Working Paper No. 219 Efficient Computation of Adjusted p-Values for Resampling-Based Stepdown Multiple Testing Joseph P. Romano and Michael Wolf February 2016 Efficient Computation of Adjusted p-Values for Resampling-Based Stepdown Multiple Testing∗ Joseph P. Romano† Michael Wolf Departments of Statistics and Economics Department of Economics Stanford University University of Zurich Stanford, CA 94305, USA 8032 Zurich, Switzerland [email protected] [email protected] February 2016 Abstract There has been a recent interest in reporting p-values adjusted for resampling-based stepdown multiple testing procedures proposed in Romano and Wolf (2005a,b). The original papers only describe how to carry out multiple testing at a fixed significance level. Computing adjusted p-values instead in an efficient manner is not entirely trivial. Therefore, this paper fills an apparent gap by detailing such an algorithm. KEY WORDS: Adjusted p-values; Multiple testing; Resampling; Stepdown procedure. JEL classification code: C12. ∗We thank Henning M¨uller for helpful comments. †Research supported by NSF Grant DMS-1307973. 1 1 Introduction Romano and Wolf (2005a,b) propose resampling-based stepdown multiple testing procedures to control the familywise error rate (FWE); also see Romano et al. (2008, Section 3). The procedures as described are designed to be carried out at a fixed significance level α. Therefore, the result of applying such a procedure to a set of data will be a ‘list’ of binary decisions concerning the individual null hypotheses under study: reject or do not reject a given null hypothesis at the chosen significance level α. -
Resampling Methods: Concepts, Applications, and Justification
Practical Assessment, Research, and Evaluation Volume 8 Volume 8, 2002-2003 Article 19 2002 Resampling methods: Concepts, Applications, and Justification Chong Ho Yu Follow this and additional works at: https://scholarworks.umass.edu/pare Recommended Citation Yu, Chong Ho (2002) "Resampling methods: Concepts, Applications, and Justification," Practical Assessment, Research, and Evaluation: Vol. 8 , Article 19. DOI: https://doi.org/10.7275/9cms-my97 Available at: https://scholarworks.umass.edu/pare/vol8/iss1/19 This Article is brought to you for free and open access by ScholarWorks@UMass Amherst. It has been accepted for inclusion in Practical Assessment, Research, and Evaluation by an authorized editor of ScholarWorks@UMass Amherst. For more information, please contact [email protected]. Yu: Resampling methods: Concepts, Applications, and Justification A peer-reviewed electronic journal. Copyright is retained by the first or sole author, who grants right of first publication to Practical Assessment, Research & Evaluation. Permission is granted to distribute this article for nonprofit, educational purposes if it is copied in its entirety and the journal is credited. PARE has the right to authorize third party reproduction of this article in print, electronic and database forms. Volume 8, Number 19, September, 2003 ISSN=1531-7714 Resampling methods: Concepts, Applications, and Justification Chong Ho Yu Aries Technology/Cisco Systems Introduction In recent years many emerging statistical analytical tools, such as exploratory data analysis (EDA), data visualization, robust procedures, and resampling methods, have been gaining attention among psychological and educational researchers. However, many researchers tend to embrace traditional statistical methods rather than experimenting with these new techniques, even though the data structure does not meet certain parametric assumptions. -
Monte Carlo Simulation and Resampling
Monte Carlo Simulation and Resampling Tom Carsey (Instructor) Jeff Harden (TA) ICPSR Summer Course Summer, 2011 | Monte Carlo Simulation and Resampling 1/68 Resampling Resampling methods share many similarities to Monte Carlo simulations { in fact, some refer to resampling methods as a type of Monte Carlo simulation. Resampling methods use a computer to generate a large number of simulated samples. Patterns in these samples are then summarized and analyzed. However, in resampling methods, the simulated samples are drawn from the existing sample of data you have in your hands and NOT from a theoretically defined (researcher defined) DGP. Thus, in resampling methods, the researcher DOES NOT know or control the DGP, but the goal of learning about the DGP remains. | Monte Carlo Simulation and Resampling 2/68 Resampling Principles Begin with the assumption that there is some population DGP that remains unobserved. That DGP produced the one sample of data you have in your hands. Now, draw a new \sample" of data that consists of a different mix of the cases in your original sample. Repeat that many times so you have a lot of new simulated \samples." The fundamental assumption is that all information about the DGP contained in the original sample of data is also contained in the distribution of these simulated samples. If so, then resampling from the one sample you have is equivalent to generating completely new random samples from the population DGP. | Monte Carlo Simulation and Resampling 3/68 Resampling Principles (2) Another way to think about this is that if the sample of data you have in your hands is a reasonable representation of the population, then the distribution of parameter estimates produced from running a model on a series of resampled data sets will provide a good approximation of the distribution of that statistics in the population.