Generalizing, Decoding, and Optimizing Support Vector Machine Classification
This work addresses the need for reduced expert intervention in machine learning pipelines, though it appears incremental as it builds on existing SVM and preprocessing methods.
The paper tackles the challenge of selecting optimal algorithms and parameters for preprocessing and classification in complex data by presenting a framework that integrates theoretical analysis, interpretability, and semiautomatic optimization, resulting in a system that interfaces with numerous algorithms to ease the optimization process.
The classification of complex data usually requires the composition of processing steps. Here, a major challenge is the selection of optimal algorithms for preprocessing and classification (including parameterizations). Nowadays, parts of the optimization process are automized but expert knowledge and manual work are still required. We present three steps to face this process and ease the optimization. Namely, we take a theoretical view on classical classifiers, provide an approach to interpret the classifier together with the preprocessing, and integrate both into one framework which enables a semiautomatic optimization of the processing chain and which interfaces numerous algorithms.