3,124 research outputs found
Linear and Order Statistics Combiners for Pattern Classification
Several researchers have experimentally shown that substantial improvements
can be obtained in difficult pattern recognition problems by combining or
integrating the outputs of multiple classifiers. This chapter provides an
analytical framework to quantify the improvements in classification results due
to combining. The results apply to both linear combiners and order statistics
combiners. We first show that to a first order approximation, the error rate
obtained over and above the Bayes error rate, is directly proportional to the
variance of the actual decision boundaries around the Bayes optimum boundary.
Combining classifiers in output space reduces this variance, and hence reduces
the "added" error. If N unbiased classifiers are combined by simple averaging,
the added error rate can be reduced by a factor of N if the individual errors
in approximating the decision boundaries are uncorrelated. Expressions are then
derived for linear combiners which are biased or correlated, and the effect of
output correlations on ensemble performance is quantified. For order statistics
based non-linear combiners, we derive expressions that indicate how much the
median, the maximum and in general the ith order statistic can improve
classifier performance. The analysis presented here facilitates the
understanding of the relationships among error rates, classifier boundary
distributions, and combining in output space. Experimental results on several
public domain data sets are provided to illustrate the benefits of combining
and to support the analytical results.Comment: 31 page
Building Combined Classifiers
This chapter covers different approaches that may be taken when building an
ensemble method, through studying specific examples of each approach from research
conducted by the authors. A method called Negative Correlation Learning illustrates a
decision level combination approach with individual classifiers trained co-operatively. The
Model level combination paradigm is illustrated via a tree combination method. Finally,
another variant of the decision level paradigm, with individuals trained independently
instead of co-operatively, is discussed as applied to churn prediction in the
telecommunications industry
Building a Document Genre Corpus: a Profile of the KRYS I Corpus
This paper describes the KRYS I corpus (http://www.krys-corpus.eu/Info.html), consisting of documents classified into 70 genre classes. It has been constructed as part of an effort to automate document genre classification as distinct from topic detection. Previously there has been very little work on building corpora of texts which have been classified using a non-topical genre palette. The reason for this is partly due to the fact that genre as a concept, is rooted in philosophy, rhetoric and literature, and highly complex and domain dependent in its interpretation ([11]). The usefulness of genre in everyday information search is only now starting to be recognised and there is no genre classification schema that has been consolidated to have applicable value in this direction. By presenting here our experiences in constructing the KRYS I corpus, we hope to shed light on the information gathering and seeking behaviour and the role of genre in these activities, as well as a way forward for creating a better corpus for testing automated genre classification tasks and the application of these tasks to other domains
Justicia: A Stochastic SAT Approach to Formally Verify Fairness
As a technology ML is oblivious to societal good or bad, and thus, the field
of fair machine learning has stepped up to propose multiple mathematical
definitions, algorithms, and systems to ensure different notions of fairness in
ML applications. Given the multitude of propositions, it has become imperative
to formally verify the fairness metrics satisfied by different algorithms on
different datasets. In this paper, we propose a \textit{stochastic
satisfiability} (SSAT) framework, Justicia, that formally verifies different
fairness measures of supervised learning algorithms with respect to the
underlying data distribution. We instantiate Justicia on multiple
classification and bias mitigation algorithms, and datasets to verify different
fairness metrics, such as disparate impact, statistical parity, and equalized
odds. Justicia is scalable, accurate, and operates on non-Boolean and compound
sensitive attributes unlike existing distribution-based verifiers, such as
FairSquare and VeriFair. Being distribution-based by design, Justicia is more
robust than the verifiers, such as AIF360, that operate on specific test
samples. We also theoretically bound the finite-sample error of the verified
fairness measure.Comment: 24 pages, 7 figures, 5 theorem
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