Patent ID: 10303737

Abstract:
Methods for discovery of a Markov boundary from data constitute one of the most important recent developments in pattern recognition and applied data analysis and modeling, primarily because they offer a principled solution to the variable/feature selection problem and give insight about local causal structure. Even though there is always a single Markov boundary of the response variable in faithful distributions, distributions with violations of the intersection property of probability theory may have multiple Markov boundaries. Such distributions are abundant in practical data-analytic applications, and there are several reasons why it is important to discover and extract all Markov boundaries from such data as a critical step of data analysis. The present invention is a novel fast generative method (termed Generalized-iTIE*) that can discover all Markov boundaries from a sample drawn from a distribution. The new method has been tested with simulated data and then applied to discover Markov boundaries in datasets from several application domains including but not limited to: biology, medicine, economics, ecology, image recognition, text processing, and computational biology.