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Abstract

A method of machine learning utilizing binary classification trees combined with Bayesian classifiers in which a training component includes creating nodes, utilizing the expectation maximization algorithm to create statistical kernels and mixtures for leaf nodes and kappa statistics to identify nodes as leaf nodes or branch nodes, creating hyperplanes for branch nodes, splitting the training data for branch nodes into subsets to be provided to subtrees, and in which an operating component traverses a binary classifier tree with a feature vector to be classified by determining for each branch node whether the feature vector lies to the left or the right of the branch node hyperplane and finally classifies the feature vector upon arriving at a leaf node by computing log likelihoods for the feature vector for each mixture in the leaf node and determining the classification of the feature vector according to the highest log likelihood, in which the preferred embodiment described is classification of internal combustion engine cylinder firings as nominal firings or misfires.

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First Claim