Removing Statistical Biases in Unsupervised Sequence Learning

Horman, Yoav; Kaminka, Gal A.

doi:10.1007/11552253_15

Cited by 2 publications

(4 citation statements)

References 8 publications

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“…A subset of preliminary results from this work appears in [9]. The authors gratefully acknowledge useful discussions with Adele Howe, Paul Cohen, and Ronen Feldman.…”

Section: Acknowledgmentsmentioning

confidence: 93%

Removing biases in unsupervised learning of sequential patterns1

Horman

Kaminka

2007

IDA

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Unsupervised sequence learning is important to many applications. A learner is presented with unlabeled sequential data, and must discover sequential patterns that characterize the data. Popular approaches to such learning include (and often combine) frequency-based approaches and statistical analysis. However, the quality of results is often far from satisfactory. Though most previous investigations seek to address method-specific limitations, we instead focus on general (methodneutral) limitations in current approaches. This paper takes two key steps towards addressing such general quality-reducing flaws. First, we carry out an in-depth empirical comparison and analysis of popular sequence learning methods in terms of the quality of information produced, for several synthetic and real-world datasets, under controlled settings of noise. We find that both frequency-based and statisticsbased approaches (i) suffer from common statistical biases based on the length of the sequences considered; (ii) are unable to correctly generalize the patterns discovered, thus flooding the results with multiple instances (with slight variations) of the same pattern. We additionally show empirically that the relative quality of different approaches changes based on the noise present in the data: Statistical approaches do better at high levels of noise, while frequency-based approaches do better at low levels of noise. As our second contribution, we develop methods for countering these common deficiencies. We show how to normalize rankings of candidate patterns such that the relative ranking of different-length patterns can be compared. We additionally show the use of clustering, based on sequence similarity, to group together instances of the same general pattern, and choose the most general pattern that covers all of these. The results show significant improvements in the quality of results in all methods, and across all noise settings.

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“…A subset of preliminary results from this work appears in [9]. The authors gratefully acknowledge useful discussions with Adele Howe, Paul Cohen, and Ronen Feldman.…”

Section: Acknowledgmentsmentioning

confidence: 93%

Removing biases in unsupervised learning of sequential patterns1

Horman

Kaminka

2007

IDA

Self Cite

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“…In Horman and Kaminka's work [13] a learner to discover sequential patterns is presented. Also, to overcome the length bias obstacle, they normalize candidate pattern ranks based on their length.…”

Section: Discussionmentioning

confidence: 99%

“…In Horman and Kaminka's work [13] a learner is presented with unlabelled sequential data, and must discover sequential patterns that characterize the data. Also, two popular approaches to such learning are evaluated: frequency-based methods [14] and statistical dependence methods [4].…”

Section: Related Work On Sequence Classificationmentioning

confidence: 99%

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Sequence Classification Using Statistical Pattern Recognition

Iglesias

Ledezma

Sanchís

Lecture Notes in Computer Science

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Abstract. Sequence classification is a significant problem that arises in many different real-world applications. The purpose of a sequence classifier is to assign a class label to a given sequence. Also, to obtain the pattern that characterizes the sequence is usually very useful. In this paper, a technique to discover a pattern from a given sequence is presented followed by a general novel method to classify the sequence. This method considers mainly the dependencies among the neighbouring elements of a sequence. In order to evaluate this method, a UNIX command environment is presented, but the method is general enough to be applied to other environments.

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Removing Statistical Biases in Unsupervised Sequence Learning

Cited by 2 publications

References 8 publications

Removing biases in unsupervised learning of sequential patterns1

Removing biases in unsupervised learning of sequential patterns1

Sequence Classification Using Statistical Pattern Recognition

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