University of Nebraska Omaha

About: University of Nebraska Omaha is a education organization based out in Omaha, Nebraska, United States. It is known for research contribution in the topics: Population & Poison control. The organization has 4526 authors who have published 8905 publications receiving 213914 citations. The organization is also known as: UNO & University of Omaha.

Age of acquisition predicts naming and lexical-decision performance above and beyond 22 other predictor variables: an analysis of 2,342 words.

Abstract: Age of acquisition (AoA) ratings were obtained and were used in hierarchical regression analyses to predict naming and lexical-decision performance for 2,342 words (from Balota, Cortese, Sergent-Marshall, Spieler, & Yap, 2004). In the analyses, AoA was included in addition to the set of predictors used by Balota et al. (2004). AoA significantly predicted latency performance on both tasks above and beyond the standard predictor set. However, AoA was more strongly related to lexical-decision performance than to naming performance. Finally, the previously reported effect of imageability on naming latencies by Balota et al. was not significant with AoA included as a factor. These results are consistent with the idea either that AoA has a semantic/lexical locus or that AoA effects emerge primarily in situations in which the input–output mapping is arbitrary.

An empirical study of classification algorithm evaluation for financial risk prediction

Abstract: A wide range of classification methods have been used for the early detection of financial risks in recent years. How to select an adequate classifier (or set of classifiers) for a given dataset is an important task in financial risk prediction. Previous studies indicate that classifiers' performances in financial risk prediction may vary using different performance measures and under different circumstances. The main goal of this paper is to develop a two-step approach to evaluate classification algorithms for financial risk prediction. It constructs a performance score to measure the performance of classification algorithms and introduces three multiple criteria decision making (MCDM) methods (i.e., TOPSIS, PROMETHEE, and VIKOR) to provide a final ranking of classifiers. An empirical study is designed to assess various classification algorithms over seven real-life credit risk and fraud risk datasets from six countries. The results show that linear logistic, Bayesian Network, and ensemble methods are ranked as the top-three classifiers by TOPSIS, PROMETHEE, and VIKOR. In addition, this work discusses the construction of a knowledge-rich financial risk management process to increase the usefulness of classification results in financial risk detection.

A Design Approach for Collaboration Processes: A Multimethod Design Science Study in Collaboration Engineering

Abstract: Collaboration engineering is an approach for the design and deployment of repeatable collaboration processes that can be executed by practitioners without the support of collaboration professionals such as facilitators. A critical challenge in collaboration engineering concerns how the design activities have to be executed and which design choices have to be made to create a process design. We report on a four-year design science study in which we developed a design approach for collaboration engineering that incorporates existing process design methods, pattern-based design principles, and insights from expert facilitators regarding design challenges and choices. The resulting approach was evaluated and continuously improved in four trials with 37 students. Our findings suggest that this approach is useful to support the design of repeatable collaboration processes. Our study further serves as an example of how a design approach can be developed and improved following a multimethod design science approach.