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Hierarchical Hidden Markov Models for User/Process Profile Learning

Galassi U., Botta M., Giordana A.

Fundamenta Informaticae

2007

Vol. 78, nr 4

487-505

This paper presents an algorithm for automatically constructing sophisticated user/process profiles from traces of their behavior. A profile is encoded by means of a Hierarchical Hidden Markov Model (HHMM), which is a well formalized tool suitable to model complex patterns in long temporal or spatial sequences. A special sub-class of this hierarchical model, oriented to user/process profiling, is also introduced. The algorithm follows a bottom-up strategy, in which elementary facts in the sequences (motifs) are progressively grouped, thus building the abstraction hierarchy of a HHMM, layer after layer. The method is firstly evaluated on artificial data. Then a user identification task, from real traces, is considered. A preliminary experimentation with several different users produced encouraging results.

Learning classification programs : the Genetic Algorithm approach

Giordana A., Lo Bello G.

Fundamenta Informaticae

1998

Vol. 35, nr 1-4

163-177

Genetic Algorithms have been proposed by many authors for Machine Learning tasks. In fact, they are appealing for several different reasons, such as the flexibility, the great exploration power, and the possibility of exploiting parallel processing. Nevertheless, it is still controversial whether the genetic approach can really provide effective solutions to learning tasks, in comparison to other algorithms based on classical search strategies. In this paper we try to clarify this point and we overview the work done with respect to the task of learning classification programs from examples. The state of the art. Emerging from our analysis suggests that the genetic approach can be a valuable alternative to classical approaches, even if further investigation is necessary in order to come to a final conclusion.