Optimizing Recommendations for Clustering Algorithms Using Meta-learning

Adam Jilling, University of Rhode Island

Abstract

The field of machine learning (ML) has seen explosive growth over the past decade, largely due to increases in technology and improvements of implementations. As powerful as ML solutions can be, they are still reliant on human input to select the optimal algorithms and parameters. This process is typically done by trial and error, as researchers will select a number of algorithms and choose whichever provides the most desirable result. This study will use a process called meta-learning to evaluate and analyze datasets and extract a series of meta-features. These features can then be used to intelligently recommend an optimal algorithm, without the cost of having to manually run the algorithm. To accomplish this, we will experiment using 230 datasets and determine their expected outcomes using only the meta-features. The outcomes being optimized are performance (accuracy) and runtime. Results are ranked in terms of performance and runtime and we can determine how accurately the learning model was able to choose the optimal algorithm for each objective. Additionally, we also run tests to determine the optimal learning rate and weight decay to use when training.

Subject Area

Computer science|Artificial intelligence

Recommended Citation

Adam Jilling, "Optimizing Recommendations for Clustering Algorithms Using Meta-learning" (2019). Dissertations and Master's Theses (Campus Access). Paper AAI27667226.
https://digitalcommons.uri.edu/dissertations/AAI27667226

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