Improved methodology was used to re-examine the weak correspondence between problem and pathological gamblers identified in population surveys and subsequent classification of these individuals in clinical interviews. The SOGS-R, the CPGI, the NODS and the Problem and Pathological Gambling Measure (PPGM), as well as questions about gambling participation and expenditures, were administered to a total of 7272 adults. Two clinicians then assessed each person's status, based on comprehensive written profiles derived from these questionnaire responses. Instrument classification was then compared to clinical classification. All four instruments correctly classified most non-problem gamblers (i.e. had good to excellent sensitivity, specificity and negative predictive power). However, the PPGM was the only instrument with good classification of problem gamblers (i.e. excellent sensitivity and positive predictive power). The CPGI and SOGS-R had weak positive predictive power and the NODS had only adequate sensitivity and positive predictive power. Improvement in the classification accuracy of the CPGI occurred when a 5+ cut-off was used and when a 4+ cut-off was used with the SOGS. In general, the classification accuracy of the NODS, SOGS and CPGI is better than prior research suggested but overall accuracy is still modest. With adjusted cut-offs, all three instruments are reasonably congruent with clinical ratings. 相似文献
A model-based classification technique is developed, based on mixtures of multivariate t-factor analyzers. Specifically, two related mixture models are developed and their classification efficacy studied. An AECM algorithm is used for parameter estimation, and convergence of these algorithms is determined using Aitken's acceleration. Two different techniques are proposed for model selection: the BIC and the ICL. Our classification technique is applied to data on red wine samples from Italy and to fatty acid measurements on Italian olive oils. These results are discussed and compared to more established classification techniques; under this comparison, our mixture models give excellent classification performance. 相似文献
Multi-criteria inventory classification groups inventory items into classes, each of which is managed by a specific re-order policy according to its priority. However, the tasks of inventory classification and control are not carried out jointly if the classification criteria and the classification approach are not robustly established from an inventory-cost perspective. Exhaustive simulations at the single item level of the inventory system would directly solve this issue by searching for the best re-order policy per item, thus achieving the subsequent optimal classification without resorting to any multi-criteria classification method. However, this would be very time-consuming in real settings, where a large number of items need to be managed simultaneously.
In this article, a reduction in simulation effort is achieved by extracting from the population of items a sample on which to perform an exhaustive search of best re-order policies per item; the lowest cost classification of in-sample items is, therefore, achieved. Then, in line with the increasing need for ICT tools in the production management of Industry 4.0 systems, supervised classifiers from the machine learning research field (i.e. support vector machines with a Gaussian kernel and deep neural networks) are trained on these in-sample items to learn to classify the out-of-sample items solely based on the values they show on the features (i.e. classification criteria). The inventory system adopted here is suitable for intermittent demands, but it may also suit non-intermittent demands, thus providing great flexibility. The experimental analysis of two large datasets showed an excellent accuracy, which suggests that machine learning classifiers could be implemented in advanced inventory classification systems. 相似文献