Data mining applied to feature selection methods for aboveground carbon stock modelling

Feature selection methods for mining bioinformatics data

Feature selection methods for mining bioinformatics data – p. 1/3

Evaluating Feature Selection Methods for Learning in Data Mining Applications

Recent advances in computing technology in terms of speed, cost, as well as access to tremendous amounts of computing power and the ability to process huge amounts of data in reasonable time has spurred increased interest in data mining applications. Machine learning has been one of the methods used in most of these data mining applications. The data used as input to any of these learning syste...

Application of Feature Selection Methods in Educational Data Mining

In the recent years, web based learning has emerged as a new field of research due to growth of network and communication technology. These learning systems generate a large volume of student data. Data mining algorithms may be applied on this data set to study interesting patterns. As an example, student enrollment data and his past examination records could be used to predict his grades in th...

Combination of Feature Selection and Learning Methods for IoT Data Fusion

In this paper, we propose five data fusion schemes for the Internet of Things (IoT) scenario,which are Relief and Perceptron (Re-P), Relief and Genetic Algorithm Particle Swarm Optimization (Re-GAPSO), Genetic Algorithm and Artificial Neural Network (GA-ANN), Rough and Perceptron (Ro-P)and Rough and GAPSO (Ro-GAPSO). All the schemes consist of four stages, including preprocessingthe data set ba...

Feature Selection in Data Mining