Evaluating Frameworks for Implementing Machine Learning in Signal Processing : A Comparative Study of CRISP-DM, SEMMA and KDD

Abstract: Machine learning is when a computer can learn from data and draw its own conclusions without being explicitly programmed to do so. To implement machine learning effectively and correctly, it is important to have a structured framework to follow. Today, there exist several different frameworks but no framework is suited for all purposes of machine learning. This thesis evaluates three chosen frameworks CRISP-DM, SEMMA and KDD for the purpose of imple- menting machine learning in signal processing. This study was conducted at Saab AB in Ja¨rf¨alla. The specific problem area of signal processing that was evaluated in the thesis was radar warn- ing systems. A hypothesis is that they could become more efficient with machine learning. To evaluate the chosen frameworks, it was studied what was demanded from a framework when implementing machine learning in the chosen problem area. The evaluation was done with a theoretical comparison where no implementations of the different frameworks were done. The frameworks were evaluated through an evaluation method created by the authors. The evaluation method was used for the purpose of finding a framework suitable for signal processing when developing the software for a radar warning system. The result is that CRISP-DM is the most well-suited of the three frame- works. This because it originates from a business perspective, is distinct in how to use it and is easy to implement in an agile process like Scrum.