An event-covering method for effective probabilistic inference

摘要

The probabilistic approach is useful in many artificial intelligence applications, especially when a certain degree of uncertainty or probabilistic variations exists in either the data or the decision process. The event-covering approach detects statistically significant event associations and can deduce a certain structure of inherent data relationships. By event-covering, we mean the process of covering or selecting statistically significant events which are outcomes in the outcome space of variable pairs, disregarding whether the variables (with regards to the complete outcome space) are statistically significant for inference or not. This approach enables us to tackle two problems well known in many artificial intelligence applications, namely: (1) the selection of useful information inherent in the data when the causal relationship is uncertain or unknown, and (2) the necessity to discover and disregard uncertain events which are erroneous or simply irrelevant. Our proposed method can be applied to a large class of decision-support tasks. By analyzing only the useful statistically significant information extracted from the event-covering process, we can formulate an effective probabilistic inference method applicable to incomplete discrete-valued (symbolic) data. The statistical patterns detected by our method then represent important empirical knowledge gained. To demonstrate the method's effectiveness in solving pattern recognition problems with incomplete data and/or data with high “noise” content (with uncertain and irrelevant events), this method has been evaluated using both simulated and real life biomolecular data.