We study experimentally the effects of cost structure and prize allocation rules on the performance of rent-seeking contests. Most previous studies use a lottery prize rule and linear cost, and find both overbidding relative to the Nash equilibrium prediction and significant variation of efforts, which we term ‘overspreading.’ We investigate the effects of allocating the prize by a lottery vers...

This paper proposes a new method to learn time based rules from telecom system alarm data for prediction of the classes of alarms. A time based rule associates an alarm class with the StartTime attribute and other attributes of alarms. The rules are evaluated with the coverage of the rules in the training data set. Given a new alarm generated at a particular time, its alarm class can be predict...

In this paper, we present an association rule based protein interaction prediction method. We use neural network to cluster protein interaction data and feature selection method to reduce protein feature dimension. After this model training, association rules for protein interaction prediction are generated by decoding a set of learned weights of trained neural network and association rule mini...

Prediction of protein interactions is one of the central problems in post–genomic biology. In this paper, we present an association rule-based protein interaction prediction method. We adopted neural network to cluster protein interaction data, and used information theory based feature selection method to reduce protein feature dimension. After model training, feature association rules are gene...

Meta-analyses comparing the accuracy of clinical versus actuarial prediction have shown actuarial methods to outperform clinical methods, on average. However, actuarial methods are still not widely used in clinical practice, and there has been a call for the development of actuarial prediction methods for clinical practice. We argue that rule-based methods may be more useful than the linear mai...

Nowadays, new methods are required to take advantage of the rich and extensive gold mine of data given the vast content of data particularly created by educational systems. Data mining algorithms have been used in educational systems especially e-learning systems due to the broad usage of these systems. Providing a model to predict final student results in educational course is a reason for usi...

Recently, applications attracted rampant attention in Epidemiology, Medical Entomology, Bio informatics, and Bio surveillance. Data mining applications is greatly useful to all stake holders in the healthcare industry. Associative Classification (AC) is a branch of data mining, a larger area of scientific study. To build a model for the purpose of prediction, AC is a suitable prediction techniq...

OBJECTIVE We sought to determine the diagnostic accuracy of clinical prediction rules to exclude acute coronary syndrome (ACS) in the emergency department (ED) setting. METHODS We searched MEDLINE, EMBASE, Web of Science and the Cochrane Database of Systematic Reviews. We contacted content experts to identify additional articles for review. Reference lists of included studies were hand search...

Prognostic scores for lower respiratory tract infections (LRTI) have been mainly derived in a hospital setting. The current authors have developed and validated a prediction rule for the prognosis of acute LRTI in elderly primary-care patients. Data including demographics, medication use, healthcare use and comorbid conditions from 3,166 episodes of patients aged > or =65 yrs visiting the gener...

Background: F-FDG PET/CT (PET) is useful in assessing inflammatory activity in sarcoidosis. However, no appropriate indications are available. The aim of this study was to develop a prediction rule that can be used to identify symptomatic sarcoidosis patients who have a high probability of PET-positivity. Methods: We retrospectively analyzed a cohort of sarcoidosis patients with non organ speci...