Meta Learning for Selection of Best Causal Discovery Algorithms
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Selection of best causal discovery algorithm for any new dataset is a difficult and time consuming process as it requires a researcher to have prior knowledge about existing standard structure learning algorithms. This research proposed a novel meta-learning approach to this problem. Meta-learning refers to learning about learning algorithms where different kinds of meta-data, such as properties of the learning problem, performance measures of different algorithms and previous patterns derived from the data are used to select the best or a combination of learning algorithms to effectively solv...
Selection of best causal discovery algorithm for any new dataset is a difficult and time consuming process as it requires a researcher to have prior knowledge about existing standard structure learning algorithms. This research proposed a novel meta-learning approach to this problem. Meta-learning refers to learning about learning algorithms where different kinds of meta-data, such as properties of the learning problem, performance measures of different algorithms and previous patterns derived from the data are used to select the best or a combination of learning algorithms to effectively solve a given learning problem. Several Bayesian networks in literature were manipulated, sampled to generate thousands of datasets, and specific features were extracted from each for meta- learning. Three standard structure learning algorithms were run on each of the generated datasets to discover underlying causal networks and their performance was evaluated. With our new techniques, we were able to implement a tool for generating many causal models, sampling many datasets from each model and later determine the best or a combination algorithms for new datasets through meta-learning.
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