Systematically false positives in early warning signal analysis

False positives in cancer epidemiology.

BACKGROUND A recent attempt to estimate the false-positive rate for cancer epidemiology studies is based on agents in International Agency for Research on Cancer (IARC) category 3 (agent not classifiable as to its carcinogenicity to humans) in the IARC Monographs Program. METHODS The estimation method is critiqued regarding biases caused by its reliance on the IARC classification criteria for...

Minimizing false positives in differential display.

Dealing with False Positives in Intrusion Detection

Many of today's Intrusion Detection Systems (IDSs) suffer from high rates of false positives [1]. False positives are a severe problem because investigating them takes time and energy. Even worse, if the load of false positives is too high, security personnel might become negligent and start to ignore alarms. Improving this situation is difficult [1, 2]. One possible solution is to use highly s...

False positives in functional near-infrared topography.

Functional cranial near-infrared spectroscopy (NIRS) has been widely used to investigate the haemodynamic changes which occur in response to functional activation. The technique exploits the different absorption spectra of oxy- and deoxy-haemoglobin ([HbO2] [HHb]) in the near-infrared region to measure the changes in oxygenation and haemodynamics in the cortical tissue. The aim of this study wa...

Controlling False Positives in Association Rule Mining

Association rule mining is an important problem in the data mining area. It enumerates and tests a large number of rules on a dataset and outputs rules that satisfy user-specified constraints. Due to the large number of rules being tested, rules that do not represent real systematic effect in the data can satisfy the given constraints purely by random chance. Hence association rule mining often...