What is feature extraction with example?

Feature extraction refers to the process of transforming raw data into numerical features that can be processed while preserving the information in the original data set . It yields better results than applying machine learning directly to the raw data.

Time-series data have core components like seasonality, trend, and cycles . For example, ice-cream sales usually have yearly seasonality — you can reasonably predict the next summer’s sales based on this year’s. Similarly, temperatures or air quality measurements have daily seasonality or also, yearly.

Feature Extraction uses an object-based approach to classify imagery, where an object (also called segment) is a group of pixels with similar spectral, spatial, and/or texture attributes . Traditional classification methods are pixel-based, meaning that spectral information in each pixel is used to classify imagery.