Robust Weighted Kernel Logistic Regression in Imbalanced and Rare Events Data

Recent developments in computing and technology, along with the availability of large amounts of raw data, have contributed to the creation of many effective techniques and algorithms in the fields of pattern recognition and machine learning. Some of the main objectives for developing these algorithms are to identify patterns within the available data or to make predictions, or both. Great success has been achieved with many classification techniques in real-life applications. Concerning binary data classification in particular, analysis of data containing rare events or disproportionate class distributions poses a great challenge to industry and to the machine learning community. This study examines rare events (REs) with binary dependent variables containing many times more non-events (zeros) than events (ones). These variables are difficult to predict and to explain as has been demonstrated in the literature. This research combines rare events corrections on Logistic Regression (LR) with truncated-Newton methods and applies these techniques on Kernel Logistic Regression (KLR). The resulting model, Rare-Event Weighted Kernel Logistic Regression (RE-WKLR) is a combination of weighting, regularization, approximate numerical methods, kernelization, bias correction, and efficient implementation, all of which enable RE-WKLR to be at once fast, accurate, and robust

Industry 4.0 benefits, challenges and critical success factors: a comparative analysis through the lens of resource dependence theory across continents and economies

Purpose. Entering a new era of digital transformation, Industry 4.0 (I 4.0) promises to revolutionize the way business has been done, providing unprecedented opportunities and challenges. This study aims to investigate empirically and comparatively analyse the benefits, challenges and critical success factors (CSFs) of Industry 4.0 across four continents and developing and developed economies. Design/methodology/approach. This study used an online survey to explore the benefits, challenges and CSFs of developed and developing economies. In order to ensure the validity of the survey, a pilot test was conducted with 10 respondents. A total of 149 participants with senior managerial, vice-presidential and directorial positions from developed and developing economies spanning four continents were invited to take part in the survey. Findings. The study ranks benefits, challenges and CSFs across economies and continents. Further, the benefit of Industry 4.0 helping to achieve organizational efficiency and agility differed across the developing and developed economies. Furthermore, the benefit improves customer satisfaction significantly differed across continents; in terms of challenges, Employee resistance to change had a higher proportion in developing economies. The future viability of I 4.0 also differed across the continents. Regarding CSFs, there was no difference across the developing and developed economies. Finally, change management and project management vary across the continents. Research limitations/implications. This study contributes to a balanced understanding of I 4.0 by providing empirical evidence for comparative analysis. Moreover, it extends the concept of resource dependence theory to explain how organizations in developing economies and developed economies deploy resources to manage external condition uncertainties to implement I 4.0. Furthermore, this study provides a structural framework to understand the specific benefits, challenges and CSFs of implementing I 4.0, which can be utilized by policymakers to promote I 4.0 in their economies or continents. Originality/value. To the best of the authors’ knowledge, no studies have empirically demonstrated the comparative analysis of benefits, challenges and CSFs across economies and continents and distinguish an original contribution of this work

Provider Reported Implementation of Nutrition-related Practices in Childcare Centers and Family Childcare Homes in Rural and Urban Nebraska

Approximately 15 million children under age 6 are in childcare settings, offering childcare providers an opportunity to influence children’s dietary intake. Childcare settings vary in organizational structure – childcare centers (CCCs) vs. family childcare homes (FCCHs) – and in geographical location – urban vs. rural. Research on the nutrition-related best practices across these childcare settings is scarce. The objective of this study is to compare nutrition-related best practices of CCCs and FCCHs that participate in the Child and Adult Care Food Program (CACFP) in rural and urban Nebraska. Nebraska providers (urban n = 591; rural n = 579) reported implementation level, implementation difficulty and barriers to implementing evidence-informed food served and mealtime practices. Chi-square tests comparing CCCs and FCCHs in urban Nebraska and CCCs and FCCHs in rural Nebraska showed sub-optimal implementation for some practices across all groups, including limiting fried meats and high sugar/ high fat foods, using healthier foods or non-food treats for celebrations and serving meals family style. Significant differences (p \u3c .05) between CCCs and FCCHs also emerged, especially with regard to perceived barriers to implementing best practices. For example, CCCs reported not having enough money to cover the cost of meals for providers, lack of control over foods served and storage problems, whereas FCCHs reported lack of time to prepare healthier foods and sit with children during mealtimes. Findings suggest that policy and public health interventions may need to be targeted to address the unique challenges of implementing evidence-informed practices within different organizational structures and geographic locations

Logistic regression in data analysis: an overview

Logistic regression (LR) continues to be one of the most widely used methods in data mining in general and binary data classification in particular. This paper is focused on providing an overview of the most important aspects of LR when used in data analysis, specifically from an algorithmic and machine learning perspective and how LR can be applied to imbalanced and rare events data.data mining; logistic regression; data classification; rare events; imbalanced data; data analysis; machine learning.

A New Fuzzy Logic Approach to Capacitated Dynamic Dial-a-Ride Problem

Almost all Dial-a-Ride problems (DARP) described in the literature pertain to the design of optimal routes and schedules for n customers who specify pick-up and drop-off times. In this article we assume that the customer is mainly concerned with the drop-off time because it is the most important to the customer. Based on the drop-off time specified by the customer and the customer’s location, a pick-up time is calculated and given to the customer by the dispatching office. We base our formulation on a dynamic fuzzy logic approach in which a new request is assigned to a vehicle. The fuzzy logic algorithm chooses the vehicle to transport the customer by seeking to satisfy two objectives. The first reflects the customer’s preference and minimizes the time a customer spends in the vehicle, and the second reflects the company’s preference and minimizes the distance a vehicle needs to travel to transport the customer. The proposed heuristic algorithm is relatively simple and computationally efficient in comparison with most deterministic algorithms for solving both small and large sized problems

Weighted logistic regression for large-scale imbalanced and rare events data, in

a b s t r a c t Latest developments in computing and technology, along with the availability of large amounts of raw data, have led to the development of many computational techniques and algorithms. Concerning binary data classification in particular, analysis of data containing rare events or disproportionate class distributions poses a great challenge to industry and to the machine learning community. Logistic Regression (LR) is a powerful classifier. The combination of LR and the truncated-regularized iteratively re-weighted least squares (TR-IRLS) algorithm, has provided a powerful classification method for large data sets. This study examines imbalanced data with binary response variables containing many more non-events (zeros) than events (ones). It has been established in the literature that these variables are difficult to predict and explain. This research combines rare events corrections to LR with truncated Newton methods. The proposed method, Rare Event Weighted Logistic Regression (RE-WLR), is capable of processing large imbalanced data sets at relatively the same processing speed as the TR-IRLS, however, with higher accuracy

Robust weighted kernel logistic regression in imbalanced and rare events data

Recent developments in computing and technology, along with the availability of large amounts of raw data, have contributed to the creation of many effective techniques and algorithms in the fields of pattern recognition and machine learning. The main objectives for developing these algorithms include identifying patterns within the available data or making predictions, or both. Great success has been achieved with many classification techniques in real-life applications. With regard to binary data classification in particular, analysis of data containing rare events or disproportionate class distributions poses a great challenge to industry and to the machine learning community. This study examines rare events (REs) with binary dependent variables containing many more non-events (zeros) than events (ones). These variables are difficult to predict and to explain as has been evidenced in the literature. This research combines rare events corrections to Logistic Regression (LR) with truncated Newton methods and applies these techniques to Kernel Logistic Regression (KLR). The resulting model, Rare Event Weighted Kernel Logistic Regression (RE-WKLR), is a combination of weighting, regularization, approximate numerical methods, kernelization, bias correction, and efficient implementation, all of which are critical to enabling RE-WKLR to be an effective and powerful method for predicting rare events. Comparing RE-WKLR to SVM and TR-KLR, using non-linearly separable, small and large binary rare event datasets, we find that RE-WKLR is as fast as TR-KLR and much faster than SVM. In addition, according to the statistical significance test, RE-WKLR is more accurate than both SVM and TR-KLR.Classification Endogenous sampling Logistic regression Kernel methods Truncated Newton

Kernel logistic regression using truncated Newton method

Classification, Logistic regression, Kernel methods, Truncated Newton method,

Truncated Newton Kernel Ridge Regression for Prediction of Porosity in Additive Manufactured SS316L

Despite the many benefits of additive manufacturing, the final quality of the fabricated parts remains a barrier to the wide adoption of this technique in industry. Predicting the quality of parts using advanced machine learning techniques may improve the repeatability of results and make additive manufacturing accessible to different fields. This study aims to integrate data extracted from various sources and use them to obtain accurate predictions of relative density with respect to the governing process parameters. Process parameters such as laser power, scan speed, hatch distance, and layer thickness are used to predict the relative density of 316L stainless steel specimens fabricated by selective laser melting. An extensive dataset is created by systematically combining experimental results from prior studies with the results of the current work. Analysis of the collected dataset shows that the laser power and scan speed significantly impact the relative density. This study compares ridge regression, kernel ridge regression, and support vector regression using the data collected for SS316L. Computational results indicate that kernel ridge regression performs better than both ridge regression and support vector regression based on the coefficient of determination and mean square error

Truncated Newton Kernel Ridge Regression for Prediction of Porosity in Additive Manufactured SS316L

Despite the many benefits of additive manufacturing, the final quality of the fabricated parts remains a barrier to the wide adoption of this technique in industry. Predicting the quality of parts using advanced machine learning techniques may improve the repeatability of results and make additive manufacturing accessible to different fields. This study aims to integrate data extracted from various sources and use them to obtain accurate predictions of relative density with respect to the governing process parameters. Process parameters such as laser power, scan speed, hatch distance, and layer thickness are used to predict the relative density of 316L stainless steel specimens fabricated by selective laser melting. An extensive dataset is created by systematically combining experimental results from prior studies with the results of the current work. Analysis of the collected dataset shows that the laser power and scan speed significantly impact the relative density. This study compares ridge regression, kernel ridge regression, and support vector regression using the data collected for SS316L. Computational results indicate that kernel ridge regression performs better than both ridge regression and support vector regression based on the coefficient of determination and mean square error