Extraction of Global Features for enhancing Machine Learning Performance

Abstract: Data Science plays an essential role in many organizations and industries to become data-driven in their decision-making and workflow, as models can provide relevant input in areas such as social media, the stock market, and manufacturing industries. To train models of quality, data preparation methods such as feature extraction are used to extract relevant features. However, global features are often ignored when feature extraction is performed on time-series datasets. This thesis aims to investigate how state-of-the-art tools and methods in data preparation and analytics can be used to extract global features and evaluate if such data could improve the performance of ML models. Global features refer to information that summarizes a full dataset such as the mean and median values from a numeric dataset. They could be used as inputs to make models understand the dataset and generalize better towards new data. The thesis went through a literature study to analyze feature extraction methods, time-series data, the definition of global features, and their benefits in bioprocessing. An effort was conducted to analyze and extract global features using tools and methods for data manipulation and feature extraction. The data used in the study consists of bioprocessing measurements of E. Coli cell growth as time-series data. The global features were evaluated through a performance comparison between models trained on a combined set of the dataset and global features, and models trained only on the full dataset. The study presents a method to extract global features with open-source tools and libraries, namely the Python language and the Numpy, Pandas, Matplot, and Scikit libraries. The quality of the global features depends on the experience in data science, data structure complexity, and domain area knowledge. The results show that the best models, trained on the dataset and global features combined, perform on average 15-18% better than models trained only on the dataset. The performance depends on the type and the number of global features combined with the dataset. Global features could be useful in manufacturing industries such as pharmaceutical and chemical, by helping models predict the inputs that lead to the desired trends and output. This could help promote sustainable production in various industries.