Balanced boosting with parallel perceptrons

The final publication is available at Springer via http://dx.doi.org/10.1007/11494669_26Proceedings of 8th International Work-Conference on Artificial Neural Networks, IWANN 2005, Vilanova i la Geltrú, Barcelona, Spain, June 8-10, 2005.Boosting constructs a weighted classifier out of possibly weak learners by successively concentrating on those patterns harder to classify. While giving excellent results in many problems, its performance can deteriorate in the presence of patterns with incorrect labels. In this work we shall use parallel perceptrons (PP), a novel approach to the classical committee machines, to detect whether a pattern’s label may not be correct and also whether it is redundant in the sense of being well represented in the training sample by many other similar patterns. Among other things, PP allow to naturally define margins for hidden unit activations, that we shall use to define the above pattern types. This pattern type classification allows a more nuanced approach to boosting. In particular, the procedure we shall propose, balanced boosting, uses it to modify boosting distribution updates. As we shall illustrate numerically, balanced boosting gives very good results on relatively hard classification problems, particularly in some that present a marked imbalance between class sizes.With partial support of Spain’s CICyT, TIC 01–572

Data-Driven Supervised Learning for Life Science Data

Life science data are often encoded in a non-standard way by means of alpha-numeric sequences, graph representations, numerical vectors of variable length, or other formats. Domain-specific or data-driven similarity measures like alignment functions have been employed with great success. The vast majority of more complex data analysis algorithms require fixed-length vectorial input data, asking for substantial preprocessing of life science data. Data-driven measures are widely ignored in favor of simple encodings. These preprocessing steps are not always easy to perform nor particularly effective, with a potential loss of information and interpretability. We present some strategies and concepts of how to employ data-driven similarity measures in the life science context and other complex biological systems. In particular, we show how to use data-driven similarity measures effectively in standard learning algorithms