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Reduced rank multinomial logistic regression in Markov chains with application to cognitive data

STATISTICS IN MEDICINE 2021年第11期40卷 2650-2664页

作者： Wang, Pei Abner, Erin L. Fardo, David W. Schmitt, Frederick A. Jicha, Gregory A. Van Eldik, Linda J. Kryscio, Richard J. Univ Kentucky Dept Stat Lexington KY 40536 USA Univ Kentucky Dept Epidemiol Lexington KY 40536 USA Univ Kentucky Alzheimers Dis Ctr Sanders Brown Ctr Aging 800 S Limestone St Lexington KY 40536 USA Univ Kentucky Dept Biostat Lexington KY 40536 USA Univ Kentucky Dept Neurol Lexington KY 40536 USA Univ Kentucky Dept Neurosci Lexington KY 40536 USA

Finite Markov chains are useful tools for studying transitions among health states;these chains can be complex consisting of a mix of transient and absorbing states. The transition probabilities, which are often affected by covariates, can be difficult to estimate due to the presence of many covariates and/or a subset of transitions that are rarely observed. The purpose of this article is to show how to estimate the effect of a subset of covariates of interest after adjusting for the presence of multiple other covariates by applying multidimensional dimension reduction to the latter. The case in which transitions within each row of the one-step transition probability matrix are estimated by multinomial logistic regression is discussed in detail. Dimension reduction for the adjustment covariates involves estimating the effect of the covariates by a product of matrices iteratively;at each iteration one matrix in the product is fixed while the second is estimated using either standard software or nonlinear estimation, depending on which of the matrices in the product is fixed. The algorithm is illustrated by an application where the effect of at least one Apolipoprotein-E (APOE) gene epsilon 4 allele on transition probability is estimated in a Markov Chain that includes adjustment for eight covariates and focuses on transitions from normal cognition to several forms of mild cognitive impairment, with possible absorption into dementia. Data were drawn from annual cognitive assessments of 649 participants enrolled in the BRAiNS cohort at the University of Kentucky's Alzheimer's Disease Research Center.

关键词： cognitive assessments dementia Markov chains multinomial logistic regression reduced rank

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FPGA Implementation of multinomial logistic regression For Vibrotactile Feedback In a Robotic Hand 8

FPGA Implementation of Multinomial Logistic Regression For V...

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8th International Conference on E-Health and Bioengineering (EHB)

作者： Erbas, Ismail Vargas, David Alejandro Guclu, Burak Bogazici Univ Inst Biomed Engn Istanbul Turkey Univ Iberoamer Ciudad de Mexico Inst Engn Ciudad De Mexico Mexico

ISBN: (纸本)9781728188034

In this study, the aim was to generate real-time non-invasive vibrotactile feedback signals using Field-Programmable Gate Array (FPGA) with multinomial logistic regression for the robotic hand. This work built a base for performing real-time vibrotactile feedback using the DESC policy, and it can be used in other FPGA studies. The hand conducted grasping type tasks with soft and hard objects and without any object. The force and bend sensors data were sent to the input ports of the National Instruments (NI) FPGA card by analog output of a NI data acquisition card to use real-time classification. The training was done in MATLAB, and the training parameters were used in FPGA for classification. The results showed that multinomial logistic regression worked very well for the object type classification. However, the accuracies for the movement and combined type classifications were not as good as offline MATLAB classifications. Accuracies can be improved with different machine learning algorithms.

关键词： FPGA Robotic Hand Vibrotactile Feedback multinomial logistic regression LabVIEW

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Prediction of spontaneous coal combustion tendency using multinomial logistic regression

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INTERNATIONAL JOURNAL OF OCCUPATIONAL SAFETY AND ERGONOMICS 2022年第4期28卷 2000-2009页

作者： Kursunoglu, Nilufer Gogebakan, Maruf Batman Univ Dept Petr & Nat Gas Engn Batman Turkey Bandirma Onyedi Eylul Univ Dept Maritime Business Adm Bandirma Turkey

Spontaneous combustion of coal is a complex underground mining disaster, which mainly threats mine safety and efficiency. Several factors usually cause spontaneous combustion of coal, such as gas concentration, ventilation and coal properties. In this study, spontaneous combustion tendencies of coal mines were predicted considering the effective parameters for an underground coal mine in Turkey. multinomial logistic regression, a multivariate statistical technique, was applied. Gas concentrations (CH4, CO, O-2) and air velocity were defined as factors affecting spontaneous coal combustion. Fire hazard levels of the coal mines were determined as 'normal situation' and 'potential combustion'. It was observed that CH4 and CO variables and CH4 x CO interaction were effective in the formation of clusters. The results indicate that Mine I is more liable to spontaneous combustion than Mine II and Mine III. At the same time, the effects of variations in factors are examined in the study.

关键词： coal mine hazard spontaneous combustion multinomial logistic regression statistical technique

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Sentiment Analysis Using multinomial logistic regression 3

Sentiment Analysis Using Multinomial Logistic Regression

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International Conference on Control, Electronics, Renewable Energy and Communications (ICCREC)

作者： Ramadhan, W. P. Novianty, Astri Setianingsih, Casi Telkom Univ Dept Comp Engn Bandung Indonesia

ISBN: (纸本)9781538616673

Data amount becomes rapidly increased in today's era. Data can be in form of text, picture, voice, and video. Social media is one factor of the data increase as everybody expresses, gives opinion, and even complains in social media. The first step is data collection used API twitter with each candidate names on Jakarta Governor Election. The collected data then became input for preprocessing step. The next step is extracted each tweet's feature to be listed. The list of features were transformed into feature vector in binary form and transformed again used Tf-idf method. Dataset consists of two kinds of data, training and testing. Training was labeled manually. K-Fold Cross Validation is used to test algorithm performance. Based on the result of the test, accuracy obtained reached 74% in average with composition of training data and testing data by 90:10. Changed folding amount gave no impact to the accuracy level.

关键词： twitter multinomial logistic regression text mining softmax regression

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Cloud-Based multinomial logistic regression for Analyzing Maternal Mortality Data in Postpartum Period 2

Cloud-Based Multinomial Logistic Regression for Analyzing Ma...

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2nd International Conference on Informatics and Computational Sciences (ICICoS)

作者： Purwahana, Radite Suryono, Suryono Suseno, Jatmiko Endro Diponegoro Univ Master Informat Syst Semarang Indonesia Diponegoro Univ Phys Dept Sci & Math Semarang Indonesia

ISBN: (纸本)9781538674406

The analysis used in dealing with maternal mortality factors in the postpartum period can be used as a reference in preventing maternal death in the postpartum period. Appropriate analysis is needed to reduce maternal mortality rates in the postpartum period. This study uses multinomial logistic regression to analyze the data of mothers dying in the postpartum period based on the main variables causing maternal death. multinomial logistic regression process is carried out by looking at data records of variables that influence maternal mortality. In the first experiment using data from midwife visits for seven days, the results of the multinomial logistic regression process with the highest maternal mortality occurred on the fourth day with anogenital variables reaching a percentage of 32.4% of the causes of maternal death. multinomial logistic regression processes are combined with cloud computing technology so that data can be processed more quickly and can be used together.

关键词： Cloud Computing multinomial logistic regression Postpartum Maternal Mortality

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Scaling multinomial logistic regression via Hybrid Parallelism 19

Scaling Multinomial Logistic Regression via Hybrid Paralleli...

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25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (KDD)

作者： Raman, Parameswaran Srinivasan, Sriram Matsushima, Shin Zhang, Xinhua Yun, Hyokun Vishwanathan, S. V. N. Univ Calif Santa Cruz Santa Cruz CA 95064 USA Univ Tokyo Tokyo Japan Univ Illinios Chicago IL USA Amazon Seattle WA USA

ISBN: (纸本)9781450362016

We study the problem of scaling multinomial logistic regression (MLR) to datasets with very large number of data points in the presence of large number of classes. At a scale where neither data nor the parameters are able to fit on a single machine, we argue that simultaneous data and model parallelism (Hybrid Parallelism) is inevitable. The key challenge in achieving such a form of parallelism in MLR is the log-partition function which needs to be computed across all K classes per data point, thus making model parallelism non-trivial. To overcome this problem, we propose a reformulation of the original objective that exploits double-separability, an attractive property that naturally leads to hybrid parallelism. Our algorithm (DS-MLR) is asynchronous and completely de-centralized, requiring minimal communication across workers while keeping both data and parameter workloads partitioned. Unlike standard data parallel approaches, DS-MLR avoids bulk-synchronization by maintaining local normalization terms on each worker and accumulating them incrementally using a token-ring topology. We demonstrate the versatility of DS-MLR under various scenarios in data and model parallelism, through an empirical study consisting of real-world datasets. In particular, to demonstrate scaling via hybrid parallelism, we created a new benchmark dataset (Reddit-Full) by pre-processing 1.7 billion reddit user comments spanning the period 2007-2015. We used DS-MLR to solve an extreme multi-class classification(1) problem of classifying 211 million data points into their corresponding subreddits. Reddit-Full is a massive data set with data occupying 228 GB and 44 billion parameters occupying 358 GB. To the best of our knowledge, no other existing methods can handle MLR in this setting.

关键词： multinomial logistic regression Stochastic Optimization Large Scale Machine Learning

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Predicting Product Quality from Operating Conditions Based on multinomial logistic regression 1

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21st International Conference on Computational Science and Its Applications (ICCSA)

作者： Vaz, Pedro Braga, Ana Cristina Sameiro Carvalho, M. Menezes, Gerardo Univ Minho ALGORITMI Ctr P-4800058 Guimaraes Portugal Bosch Car Multimedia Braga Portugal

ISBN: (数字)9783030869731

ISBN: (纸本)9783030869731;9783030869724

The main objective of this study is to evaluate the influence of several industrial operating conditions in the occurrence of the defect (air bubbles) in a wet bonding process of two pieces (a glass and an electronic display). The study of the influence of operating conditions in business targets is of great interest to the community of process engineers, to ensure the long-term stability of the process, as this involves complex factors. It is also relevant for the development of Data-driven approaches to support Real-Time System Monitoring. In this work, we propose to build a multinomial logistic regression model that can assess the probability of a bonded kit (glass and electronic display) having a defect in the filling material, taking into account process operating conditions. The study was conducted in a retrospective evaluation and consisted of 659 bonded kits randomly selected from a reference period. We evaluated multiple factors in the construction of a multinomial logistic regression model with 3 logit functions. The technique used to select variables to be included in the model, was the stepwise technique by choosing the smallest p-value for the variable entering in the model. Of the modelling process through multinomial logistic regression have resulted five operating conditions that have statistical significance and that can contribute as an auxiliary for the prediction of a defect.

关键词： multinomial logistic regression Logit Process improvement Process monitoring

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Predicting Unit Testing Effort Levels of Classes: An Exploratory Study based on multinomial logistic regression Modeling

Predicting Unit Testing Effort Levels of Classes: An Explora...

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International Conference on Soft Computing and Software Engineering (SCSE)

作者： Badri, Mourad Toure, Fadel Lamontagne, Luc Univ Quebec Trois Rivieres Dept Math & Comp Sci Trois Rivieres PQ GA9 5H7 Canada Univ Laval Dept Comp Sci & Software Engn Quebec City PQ G1K 7P4 Canada

The study aims at investigating empirically the ability of a Quality Assurance Indicator (Qi), a metric that we proposed in a previous work, to predict different levels of unit testing effort of classes in object-oriented systems. To capture the unit testing effort of classes, we used four metrics to quantify various perspectives related to the code of corresponding unit test cases. Classes were classified, according to the involved unit testing effort, in five categories (levels). We collected data from two open source Java software systems (ANT and JFREECHART) for which JUnit test cases exist. In order to explore the ability of the Qi metric to predict different levels of the unit testing effort of classes, we decided to explore the possibility of using the multinomial logistic regression (MLR) method. The performance of the Qi metric has been compared to the performance of three well-known source code metrics related respectively to size, complexity and coupling. Results suggest that the MLR model based on the Qi metric is able to accurately predict different levels of the unit testing effort of classes. (C) 2015 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://***/licenses/by-nc-nd/4.0/).

关键词： Object-Oriented Systems Software Testability Unit Testing Effort JUnit Code Metrics Prediction multinomial logistic regression Empirical Analysis

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Data Imputation of Brain MRI Features with Enhanced multinomial logistic regression for Alzheimer's Disease Classification 21

Data Imputation of Brain MRI Features with Enhanced Multinom...

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6th International Conference on Sustainable Information Engineering and Technology (SIET) - Innovation for Smart Societies

作者： Naganandhini, S. Shanmugavadivu, P. Kanimozhi, G. Kavitha, Muthu Subash GTN Arts Coll Dept Comp Sci Dindigul 624005 India Gandhigram Rural Inst Deemed Be Univ Dept Comp Sci & Applicat Gandhigram 624302 India Nagasaki Univ Sch Informat & Data Sci 1-14 Bunkyo Machi Nagasaki Japan

ISBN: (纸本)9781450384070

Alzheimer's disease (AD) is an immedicable neurodegenerative disease, while the collective cognitive functions deteriorate over time and turns out to be fatal. The analysis of brain's features depicted by Magnetic Resonance Image (MRI) for early diagnosis of AD has led to the development of new diagnostic and therapeutic strategies. The problem of missing data in the experimental datasets predominantly impedes the accuracy of any classification model. However, many of the existing classification techniques do not engage any mechanism to handle such missing data. In this study, we propose a novel Enhanced multinomial logistic regression (EMLR) technique, with imputation to diagnose the stages for AD. This study provides a general classification framework on the stages of AD as mild AD, moderate AD, non-demented, and very mild AD. The input dataset is treated with data imputation to fill in the missing values, transformed by one-hot encoding into vectors, and is fed into the optimized MLR model. The experiments were performed for multi-class classification of AD on OASIS brain MRI dataset features using for simple multinomial logistic regression (MLR) with and without data imputation and EMLR with data imputation and the results are recorded. The classification accuracy of EMLR model has outperformed its competitive classifiers with 88.39% accuracy. Hence, this model can be further developed into a reliable and robust interactive expert system for AD detection and classification.

关键词： Data Imputation multinomial logistic regression Alzheimer's disease Classification Brain patterns

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Ideal Step Size Estimation for the multinomial logistic regression 15

Ideal Step Size Estimation for the Multinomial Logistic Regr...

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15th Latin American Symposium on Circuits and Systems (LASCAS)

作者： Ramirez, Gabriel Rodriguez, Paul Pontificia Univ Catolica Peru Dept Elect Engn Lima Peru

ISBN: (纸本)9798350381221;9798350381238

At the core of deep learning optimization problems reside algorithms such as Stochastic Gradient Descent (SGD), which employs a subset of the data per iteration to estimate the gradient in order to minimize a cost function. Adaptive algorithms, based on SGD, are well known for being effective in using gradient information from past iterations, generating momentum or memory that enables a more accurate prediction of the true gradient slope in future iterations, thus accelerating convergence. Nevertheless, these algorithms still need an initial (scalar) learning rate (LR) as well as a LR scheduler. In this work we propose a new SGD algorithm that estimates the initial (scalar) LR via an adaptation of the ideal Cauchy step size for the multinomial logistic regression;furthermore, the LR is recursively updated up to a given number of epochs, after which a decaying LR scheduler is used. The proposed method is assessed for several well-known multiclass classification architectures and favorably compares against other well-tuned (scalar and spatially) adaptive alternatives, including the Adam algorithm.

关键词： Deep learning stochastic gradient descent adaptive step size multinomial logistic regression

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