Feature Engineering for Data Streams

It is well known that the performance of machine learning algorithms strongly depends on the feature representation of the input data [4]. A good set of features provides tremendous flexibilities that allow us to choose fast and simple models. However, the raw representation of data is not usually amenable to learning [13]. Feature engineering is the process to generate new features from the existing raw features by discovering hidden patterns in the data [65]. It aims to enrich the current feature set and increase the predictive power of the learning algorithms consequently. Therefore, feature engineering plays an important role in the success of machine learning in practice:“At the end of the day, some machine learning projects succeed and some fail. What makes the difference? Easily the most important factor is the features used [13].”