Knowledge Extraction with Interval Temporal Logic Decision Trees
Guido Sciavicco Stan Ionel Eduard
Multivariate temporal, or time, series classification is, in a way, the
temporal generalization of (numeric) classification, as every instance is
described by multiple time series instead of multiple values. Symbolic
classification is the machine learning strategy to extract explicit knowledge
from a data set, and the problem of symbolic classification of multivariate
temporal series requires the design, implementation, and test of ad-hoc machine
learning algorithms, such as, for example, algorithms for the extraction of
temporal versions of decision trees. One of the most well-known algorithms for
decision tree extraction from categorical data is Quinlan's ID3, which was
later extended to deal with numerical attributes, resulting in an algorithm
known as C4.5, and implemented in many open-sources data mining libraries,
including the so-called Weka, which features an implementation of C4.5 called
J48. ID3 was recently generalized to deal with temporal data in form of
timelines, which can be seen as discrete (categorical) versions of multivariate
time series, and such a generalization, based on the interval temporal logic
HS, is known as Temporal ID3. In this paper we introduce Temporal C4.5, that
allows the extraction of temporal decision trees from undiscretized
multivariate time series, describe its implementation, called Temporal J48, and
discuss the outcome of a set of experiments with the latter on a collection of
public data sets, comparing the results with those obtained by other,
classical, multivariate time series classification methods.