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Web-based knowledge acquisition to solve inverse problems

Machine learning is the science of building predictors from data randomly sampled from an assumed probability distribution while accounting for the computational complexity of the learning algorithm and the predictor’s performance on future data. Much of the work in machine learning is empirical and prone to errors because what the machine learns depends on the adequacy and trustworthiness of the training data. Automated web-based knowledge acquisition can play a useful role in developing systematic methods for solving a class of machine learning problems in which there is insufficient or untrustworthy data. Such problems can be solved by using a regularization procedure a mathematical strategy that seeks to supply the ”missing data.” There are several ways of regularizing a problem. Statistical methods, for example, can fill in a few data items. But these methods rely on using the available, and possibly unreliable, data to calculate the missing values. Besides, they perform poorly if the percentage of missing values exceeds a threshold. An alternative is to fill in the missing data by an automated knowledge discovery process via mining the WWW. This novel procedure is applied by first restoring missing information and next learning the structure and parameters of the unknown system from the restored data. Using a Bayesian network as a possible model for the unknown system, the parameters, i.e., the probabilities associated with the causal relationships in the network, are deduced using the knowledge mined from the WWW in conjunction with the data available on hand. The method, when tested against heart disease data sets from the UC Irvine Machine Learning Repository [UCI], gave satisfactory results. Preliminary results of our approach, using the Naive Bayes as the system model, are then compared with the performance of the EM algorithm, a wellunderstood statistical method. Work is currently in progress to assess the performance of this method in data-poor domains.