Noisy Sequence Classification with Smoothed Markov Chains

This paper is concerned with sequence classification using Markov chains when classification noise is included in the learning data. These models offer a direct generalization of a Multinomial Naive Bayes classifier by taking into account dependences between successive events up to a certain history length. Our study shows that smoothed Markov chains are very robust to classification noise. The relation between classification accuracy and test set perplexity, often used to measure prediction quality, is discussed. The influence of varying the model order is also studied from an experimental viewpoint. Experiments are conducted both on a gender classification task from spelling of first names and splicing region classification in DNA sequences. The first set of experiments also illustrate the superiority of smoothed Markov chains to classify noisy sequence over an automaton learning technique using boosting.