Skip to main content

Table 1 Description application of feature selection

From: Selecting critical features for data classification based on machine learning methods

Subject

Description

Climate, Ecology, and Environmental

The analysis of noisy ecological data [25], variables in ecology modelling [26], number of counts termites [27], community ecology and integrating species, traits, environmental, space [28], parameter in rainfall forecasting [29, 30], global climate zone [31], local climate zone [32], environmental noise pollution [33], urban pollution [34, 35], rainfall spatial temporal [36], flash flood hazard [37, 38], landslide [39], earthquake damage detection using curvilinear features [40], earthquake classifiers using stochastic reconstruction [41] and tsunami [42]

Health

Future genetic association studies of colorectal cancer [11], Aortic Anatomy on Endovascular Aneurysm Repair (EVAR) [43], colorectal cancer cases phenotype [10], identify a wide range of predictors that could enhance prediabetes prediction and clinical decision-making [44], the classification of diabetes mellitus [45], type 2 diabetes within 2 years in an obese, hypertensive population [46], the principal purpose of coronary illness [47], heart disease [48], cardiovascular disease [49], ovarian cancer patients [50], gene expression RNA-Seq data [51, 52], adjuvant chemotherapy effectiveness assessment in non-small cell lung cancer [53], and Alzheimer’s disease [54]

Finance

Mineral prospect [12], Industrial recommendation system [13], financial crisis [55], industrial coal mine [56], poverty classification [57], spatiotemporal poverty [58], potential tax fraudsters [59], risk control in financial marketing [60], electrical load consumption [61], price forecast of electrical power systems [62], electrical load data [63], electrical circuits [64], stochastic modelling [65], dynamic financial distress [66], Household indebt [67], social vulnerability [68], construction of social vulnerability index [69], financial statement fraud [70], insurance fraud [71], macroeconomic Influencers [72], stock markets [73]