Feature space reduction method for ultrahigh-dimensional, multiclass data: Random forest-based multiround screening (RFMS)

In recent years, several screening methods have been published for ultrahigh-dimensional data that contain hundreds of thousands of features, many of which are irrelevant or redundant. However, most of these methods cannot handle data with thousands of classes. Prediction models built to authenticate users based on multichannel biometric data result in this type of problem. In this study, we present a novel method known as random forest-based multiround screening (RFMS) that can be effectively applied under such circumstances. The proposed algorithm divides the feature space into small subsets and executes a series of partial model builds. These partial models are used to implement tournament-based sorting and the selection of features based on their importance. This algorithm successfully filters irrelevant features and also discovers binary and higher-order feature interactions. To benchmark RFMS, a synthetic biometric feature space generator known as BiometricBlender is employed. Based on the results, the RFMS is on par with industry-standard feature screening methods, while simultaneously possessing many advantages over them

Idősor vizualizációs webalkalmazás

Alkalmazás, amely azért jött létre, hogy egyszerű megoldást adjon idősoros adatok rögzítésére, tárolására és az adatok megjelenítésére

Detection of mild cognitive impairment based on mouse movement data of trail making test

Mild cognitive impairment (MCI) has 10%–20% prevalence in the population above the age of 65, and a significant portion of these people will go on to develop dementia later in their lives. However, if MCI is detected early, preventative measures can be taken to delay the onset of severe symptoms. Current diagnostic methods for MCI are not suitable for regular wide-scale screening. Advances in machine learning algorithms in combination with digital movement data offer rich possibilities for automated MCI detection. This paper introduces a machine learning model that effectively predicts MCI based on only a few seconds of computer mouse movement. To our knowledge, studies directly comparable to ours have not been done before. On a dataset of 70 participants, we demonstrated 80% accuracy in distinguishing healthy controls from patients with MCI. This gives an opportunity to develop a cost-efficient and easy-to-use screening method that could aid the work of healthcare professionals