Pamplin College of Business

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https://hdl.handle.net/10919/5549
The Pamplin College of Business generates high quality applied and theoretical research that supports superior teaching and business applications. Graduates of the Pamplin College, applying their analytical and decision making skills, help businesses create solutions, enhancing their competitiveness in the global business environment and improving the lives of their families and society.

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Browsing Pamplin College of Business by Author "Abrahams, Alan"

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    Formal definition of the MARS method for quantifying the unique target class discoveries of selected machine classifiers
    Restrepo, Felipe; Mali, Namrata; Abrahams, Alan; Ractham, Peter (F1000 Research, 2022-07)
    Conventional binary classification performance metrics evaluate either general measures (accuracy, F score) or specific aspects (precision, recall) of a model's classifying ability. As such, these metrics, derived from the model's confusion matrix, provide crucial insight regarding classifier-data interactions. However, modern- day computational capabilities have allowed for the creation of increasingly complex models that share nearly identical classification performance. While traditional performance metrics remain as essential indicators of a classifier's individual capabilities, their ability to differentiate between models is limited. In this paper, we present the methodology for MARS (Method for Assessing Relative Sensitivity/ Specificity) ShineThrough and MARS Occlusion scores, two novel binary classification performance metrics, designed to quantify the distinctiveness of a classifier's predictive successes and failures, relative to alternative classifiers. Being able to quantitatively express classifier uniqueness adds a novel classifier-classifier layer to the process of model evaluation and could improve ensemble model-selection decision making. By calculating both conventional performance measures, and proposed MARS metrics for a simple classifier prediction dataset, we demonstrate that the proposed metrics' informational strengths synergize well with those of traditional metrics, delivering insight complementary to that of conventional metrics.
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    Injury prevention for older adults: A dataset of safety concern narratives from online reviews of mobility-related products
    Restrepo, Felipe; Mali, Namrata; Sands, Laura P.; Abrahams, Alan; Goldberg, David M.; White, Janay; Prieto, Laura; Ractham, Peter; Gruss, Richard; Zaman, Nohel; Ehsani, Johnathon P. (Elsevier, 2022-06)
    Older adults are among the fastest-growing demographic groups in the United States, increasing by over a third this past decade. Consequently, the older adult consumer prod-uct market has quickly become a multi-billion-dollar in-dustry in which millions of products are sold every year. However, the rapidly growing market raises the poten-tial for an increasing number of product safety concerns and consumer product-related injuries among older adults. Recent manufacturer and consumer injury prevention efforts have begun to turn towards online reviews, as these provide valuable information from which actionable, timely intelligence can be derived and used to detect safety concerns and prevent injury. The presented dataset contains 1966 curated online product reviews from consumers, equally distributed between safety concerns and non-concerns, pertaining to product categories typically intended for older adults. Identified safety concerns were manually sub-coded across thirteen dimensions designed to capture relevant aspects of the consumer's experience with the purchased product, facilitate the safety concern identification and sub-classification process, and serve as a gold-standard, balanced dataset for text classifier learning. (c) 2022 The Author(s). Published by Elsevier Inc. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/)