  Frequent temporal patterns discovered in time-interval-based multivariate
data, although syntactically correct, might be non-transparent: For some
pattern instances, there might exist intervals for the same entity that
contradict the pattern's usual meaning. We conjecture that non-transparent
patterns are also less useful as classification or prediction features. We
propose a new pruning constraint during a frequent temporal-pattern discovery
process, the Semantic Adjacency Criterion [SAC], which exploits domain
knowledge to filter out patterns that contain potentially semantically
contradictory components. We have defined three SAC versions, and tested their
effect in three medical domains. We embedded these criteria in a
frequent-temporal-pattern discovery framework. Previously, we had informally
presented the SAC principle and showed that using it to prune patterns enhances
the repeatability of their discovery in the same clinical domain. Here, we
define formally the semantics of three SAC variations, and compare the use of
the set of pruned patterns to the use of the complete set of discovered
patterns, as features for classification and prediction tasks in three
different medical domains. We induced four classifiers for each task, using
four machine-learning methods: Random Forests, Naive Bayes, SVM, and Logistic
Regression. The features were frequent temporal patterns discovered in each
data set. SAC-based temporal pattern-discovery reduced by up to 97% the number
of discovered patterns and by up to 98% the discovery runtime. But the
classification and prediction performance of the reduced SAC-based
pattern-based features set, was as good as when using the complete set. Using
SAC can significantly reduce the number of discovered frequent interval-based
temporal patterns, and the corresponding computational effort, without losing
classification or prediction performance.
