Emerging Feature Engineering Trends for Machine Learning

Dear Colleagues,

Feature engineering is a crucial process aimed at building high-quality data representations from raw data that accurately capture the nature of the problem. Quality data directly impacts the performance of machine learning algorithms by improving their efficiency. In the case of inference algorithms, feature engineering could lead to interpretable models.

Although there are various theories about which techniques belong to the feature engineering process, the truth is that this stage always goes hand in hand with others, such as data cleaning. In this sense, it is possible to say that the processing techniques are firmly related, so the success of each of them depends on the previous or later stages. Additionally, the datasets present a mixture of problems that require the union of various preprocessing techniques from different areas.

In big data, the term smart data has recently emerged, where, as in standard-sized data, obtaining quality data also represents a key element since they provide veracity and validity. However, traditional methods are inefficient when applied to big data since its spatial and temporal complexity increases. This represents a challenge since it is necessary to develop and/or adapt feature engineering techniques that take into account the volume of data and the technologies, programming paradigms, and available platforms.

This Special Issue aims to provide comprehensive coverage on new and state-of-the-art feature engineering and data preprocessing methods for standard and big data problems. Authors are encouraged to submit papers on topics including (but not limited to):

• Data cleaning;
• Data imputation;
• Data normalization;
• Data transformation;
• Data reduction.

Prof. Dr. José Salvador Sánchez Garreta
Prof. Dr. Vicente García
Guest Editors

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