This paper presents a novel active learning technique aimed at the selection of sequences for manual annotation from a database of unlabelled sequences. Supervised machine learning algorithms can employ these sequences to build better models than those based on using random sequences for training. The main contribution of the proposed method is the use of sequential pattern information contained in the database to select representative and diverse sequences for annotation. These two characteristics ensure the proper coverage of the instance space of sequences and, at the same time, avoids over-fitting the trained model. The approach, called SPIAL (Sequential Pattern Information for Active Learning), uses sequential pattern mining algorithms to extract frequently occurring sub-sequences from the database and evaluates how representative and diverse each sequence is, based on this information. The output is a list of sequences for annotation sorted by representativeness and diversity. The algorithm is modular and, unlike most current techniques, it is independent from the sequential machine learning technique that builds the model as SPIAL does not need to know the set of the features taken into account by the training algorithm. Experiments done on well-known benchmarks involving sequential data show that the models trained using SPIAL increase their convergence speed while reducing manual effort by selecting small sets of very informative sequences for annotation. In addition, the computation cost using SPIAL is much lower than for the state-of-the-art algorithms evaluated.

This paper applies the Behavior Pattern Learning (BPL) approach to automatically discover the conditions previous to specific macroeconomic events from the historical evolution of macroeconomic variables, as well as historical events. Every time the target macroeconomic event (e.g. Consumer Price Index (CPI) inflation greater than 5.5%) takes place, BPL constructs several snapshots at specific previous times. Each snapshot, called a behavior summary, includes information about its present, past, and future. The set of behavior summaries is used by a regression tree approach to discover the conditions for the target event to take place. In this paper, we used BPL to automatically discover the conditions for the occurrence of specific events associated with the CPI inflation and Gross domestic product (GDP) deflactor using macroeconomic data, and war and political events. Comparisons with other prediction models on the same problem show that the BPL provides automatic forecasts that are similar in performance as those forecasts done by models assisted by experts.

A new algorithm for incremental construction of binary regression trees is presented. This algorithm, called SAIRT, adapts the induced model when facing data streams involving unknown dynamics, like gradual and abrupt function drift, changes in certain regions of the function, noise, and virtual drift. It also handles both symbolic and numeric attributes. The proposed algorithm can automatically adapt its internal parameters and model structure to obtain new patterns, depending on the current dynamics of the data stream. SAIRT can monitor the usefulness of nodes and can forget examples from selected regions, storing the remaining ones in local windows associated to the leaves of the tree. On these conditions, current regression methods need a careful configuration depending on the dynamics of the problem. Experimentation suggests that the proposed algorithm obtains better results than current algorithms when dealing with data streams that involve changes with different speeds, noise levels, sampling distribution of examples, and partial or complete changes of the underlying function.