To derive useful information from complicated data, many hybrid modeling strategies that combine nonparametric and parametric methods have been proposed. In this study, we propose a new hybrid modeling strategy that combines the random forest and the x-means methods using linear regression analysis. This strategy is referred to as XR regression.This study has three purposes: to improve the performance of a strategy of hybrid modeling using the random forest method, to determine an optimal class automatically using the x-means method, and to compare the prediction accuracy of this method with that of other existing methods.To determine the characteristics of XR regression, we compare its prediction accuracy with that of the existing methods using Monte Carlo simulations.The simulation results show that XR regression has a high performance in any situation, especially in data sets that include interaction effects.
Currently, mass production of products has become possible owing to the advances in manufacturing technology, enabling the manufacture of products in larger quantities and at higher rates than that done previously. The inspection processes for detecting nonconforming products must be performed at higher speeds without any loss in accuracy. This has motivated the use of online inspection machines such as cameras that detect defective products in many inspection processes. However, accurate criteria must be set for such online inspection machines to distinguish between the non-defective and defective products. In this study, we focus on inspection techniques used to improve the classification accuracy of an actual inspection process. While previous studies have improved the classification accuracy by using a significant amount of preprocessing and feature-extraction calculations, neither to what extent each method affects the accuracy nor how to combine the different methods to improve the accuracy is known. We introduce orthogonal arrays to test the effect of several factors by performing a few experiments. In this study, we construct an optimal combination of methods using the orthogonal array to achieve a higher accuracy and demonstrate that the resulting combination of methods does, achieve a higher classification accuracy than the previous methods.
The purpose of this research is to provide an effective model that can be used to improve the design quality of technical-service support software and thus enhance task quality. Herein, variables and model derived from previous studies are applied to a technical-service model that creates a direct chain of linkage from user contexts and software quality to overall task quality. In order to confirm its effectiveness, the proposed model is then appliedinto six types of technical-service support software.The software development for enhancing “information believability” was found to be the most effective key to task quality enhancement. In contrast, improvements to integrity and reliability do not contribute to enhancing task quality, even though their needs, in terms of technical-service support software, cannot be neglected. The proposed model can be used to determine acceptable target quality in technical-service software development situations. Because the proposed measuring model treats engineer task recognition as user context information and can be used for engineer task-quality evaluation, it can precisely indicate not only the influence of a software quality as it relates to task quality but also the quality needs and the degree to which it needs to be validated.
In this study, we estimate the propagation patterns of influenza using prescription data obtained from pharmacies in multiple areas. In our model, we assume that a peak in the volume of sales of medicine corresponds to a peak in an influenza epidemic, and we use a cross-correlation function to estimate propagation patterns by estimating the interregional gap in the time-series of the volume of sales of anti-influenza medicine. We also examine the causal relation between different time-series using the Granger causal test. Based on the propagation patterns estimated from these causal relations, we determine that the influenza virus consistently spreads outward from city centers. Finally, we assess the reproducibility of results obtained from the two estimation methods used in this study using a stochastic SIR model.