Costs Of False Negative Errors Research Articles

Specificity and item endorsement rates of personality assessment inventory over-reporting scales across ethnic, gender, and diagnostic groups in a forensic inpatient sample

The accurate evaluation of response style, particularly with respect to overreporting, is imperative in forensic settings wherein an external incentive to feign exists. Given the high cost of false positive errors in this context, as well as the associated cost of false negative errors, evaluators need to ensure that overreporting methods are effective with the unique patient populations with whom they work. Complicating this issue is that forensic samples often differ in predictable ways from the normative samples upon which typical psychological assessment instruments were normed. The purpose of the present investigation was to evaluate the specificity of the overreporting indices on the Personality Assessment Inventory, one of the most commonly used personality inventories, in a forensic sample with no ostensible incentive to feign. Although item endorsement and configural elevations on the Negative Impression Management (NIM) scale and the Malingering Index (MAL) were associated with genuine psychopathology, results indicated that the overall specificity estimates across groups were generally adequate. Further, and consistent with prior research, Rogers Discriminant Function (RDF) performed poorly in this sample.

Effects of misclassification costs on mapping mineral prospectivity

Mineral prospectivity mapping is a classification process because in a given study area, a specific region is classified as either a prospective or non-prospective area. The cost of false negative errors differs from the cost of false positive errors because false positive errors lead to wasting much more financial and material resources, whereas false negative errors result in the loss of mineral deposits. Traditional machine learning algorithms using for mapping mineral prospectivity are aimed to minimize classification errors and ignore the cost-sensitive effects. In this study, the effects of misclassification costs on mapping mineral prospectivity are explored. The cost-sensitive neural network (CSNN) for minimizing misclassification costs is applied to map Fe polymetallic prospectivity in China’s southwestern Fujian metalorganic belt (SFMB). A CSNN with a different cost ratio ranging from 1:10 to 10:1 was used to represent various misclassification costs. The cross-validation results indicated a lower misclassification cost compared to traditional neural networks through a threshold-moving based CSNN. The CSNN’s predictive results were compared to those of a traditional neural network, and the results demonstrate that the CSNN method is useful for mapping mineral prospectivity. The targets can be used to further explore undiscovered deposits in the study area.

The neural network models for IDS based on the asymmetric costs of false negative errors and false positive errors

Abstract This paper investigates the asymmetric costs of false positive and negative errors to enhance the IDS performance. The proposed method utilizes the neural network model to consider the cost ratio of false negative errors to false positive errors. Compared with false positive errors, false negative errors incur a greater loss to organizations which are connected to the systems by networks. This method is designed to accomplish both security and system performance objectives. The results of our empirical experiment show that the neural network model provides high accuracy in intrusion detection. In addition, the simulation results show that the effectiveness of intrusion detection can be enhanced by considering the asymmetric costs of false negative and false positive errors.