Introduction to Feature Selection for Atmospheric Quality Parameters Forecasting

Knowledge is only valuable when it can be used efficiently and effectively, therefore knowledge management is increasingly being recognized as a key element in extracting its value. Feature selection and dimensionality reduction can be used for that purpose, in order to reduce the time required to perform data mining and to increase the resulting classification accuracy. This paper presents an introduction to some of these algorithms that can be used to forecast atmospheric quality parameters.