Adaptive Algorithm Selector Meta-learning

Opting the most applicable machine learning algorithm for a dataset is a complex and time- consuming task. Different algorithms perform else depending on the structural parcels of the dataset. The No Free Lunch Theorem shows that no one algorithm can do better than all others for every possible problem. Thus, intelligent model selection mechanisms are needed to reduce homemade trial and computational cost. This paper proposes an Adaptive Algorithm Chooser (AAS) based on Meta-Learning. The system studies past learning gests and dataset characteristics to recommend the most suitable algorithm for new datasets. The proposed frame includes dataset characterization, meta- point birth, meta-database construction, meta-model training, and intelligent recommendation. Experimental evaluation shows that the adaptive chooser improves effectiveness and reduces trial-and-error sweats compared to traditional approaches.