Representation of a granule, relation and operation between two granules are mainly researched in granular computing. Hyperbox granular computing classification algorithms (HBGrC) are proposed based on interval analysis. Firstly, a granule is represented as the hyperbox which is the Cartesian product of $N$ intervals for classification in the $N$-dimensional space. Secondly, the relation betwee...

Identification and mapping of the significant alterations are the main objectives of the exploration geochemical surveys. The field study is time-consuming and costly to produce the classified maps. Therefore, the processing of remotely sensed data, which provide timely and multi-band (multi-layer) data, can be substituted for the field study. In this study, the ASTER imagery is used for altera...

The identification of tree species can provide a useful and efficient tool for forest managers for planning and monitoring purposes. Hyperspectral data provide sufficient spectral information to classify individual tree species. Two non-parametric classifiers, support vector machines (SVM) and random forest (RF), have resulted in high accuracies in previous classification studies. This research...

A rapid and nondestructive tea classification method is of great significance in today’s research. This study uses fluorescence hyperspectral technology machine learning to distinguish Oolong by analyzing the spectral features wavelength ranging from 475 1100 nm. The data are preprocessed multivariate scattering correction (MSC) standard normal variable (SNV), which can effectively reduce impac...

This paper presents a machine learning model using random forest (RF) algorithm with the recursive feature elimination (RFE) for soil liquefaction prediction. The prediction is tested on 253 CPT-based field data from different earthquakes. RFE, which one of selection methods, was adopted eliminating irrelevant features in mentioned dataset, and then performance RFE-RF (i.e., determined by RFE m...

In this study we evaluated whether the random forest (RF) algorithm can accurately discriminate between healthy trees and the early stages of Sirex noctilio infestation using resampled HYMAP data. More specifically, we examined the potential role of three variable selection methods (a filter, the random forest out of bag samples, and a wrapper) to produce the smallest subset of shortwave infrar...

Forests play a crucial role in maintaining the balance of global ecosystem by sustaining interactions between living and non-living entities. Changes forest areas encompass both growth loss, often driven development activities. Assessing cover its changes is also pivotal issue management. Therefore, this study aims to investigate performance machine learning algorithms, namely Random Forest (RF...

Machine learning with classification can effectively be applied for many applications, especially those with complex measurements. Therefore classification technique can be used for prediction of diseases like cancer, liver disorders and heart disease etc which involve complex measurements. This is part of growing demand and much interesting towards predictive diagnosis. It has also been establ...

Increasingly, forest management and conservation plans require spatially explicit information within a management or conservation unit. Forest biomass and potential productivity are critical variables for forest planning and assessment in the Pacific Northwest. Their values are often estimated from ground-measured sample data. For unsampled locations, forest analysts and planners lack forest pr...