9,963 research outputs found
Are screening methods useful in feature selection? An empirical study
Filter or screening methods are often used as a preprocessing step for
reducing the number of variables used by a learning algorithm in obtaining a
classification or regression model. While there are many such filter methods,
there is a need for an objective evaluation of these methods. Such an
evaluation is needed to compare them with each other and also to answer whether
they are at all useful, or a learning algorithm could do a better job without
them. For this purpose, many popular screening methods are partnered in this
paper with three regression learners and five classification learners and
evaluated on ten real datasets to obtain accuracy criteria such as R-square and
area under the ROC curve (AUC). The obtained results are compared through curve
plots and comparison tables in order to find out whether screening methods help
improve the performance of learning algorithms and how they fare with each
other. Our findings revealed that the screening methods were useful in
improving the prediction of the best learner on two regression and two
classification datasets out of the ten datasets evaluated.Comment: 29 pages, 4 figures, 21 table
A Decision tree-based attribute weighting filter for naive Bayes
The naive Bayes classifier continues to be a popular learning algorithm for data mining applications due to its simplicity and linear run-time. Many enhancements to the basic algorithm have been proposed to help mitigate its primary weakness--the assumption that attributes are independent given the class. All of them improve the performance of naĆÆve Bayes at the expense (to a greater or lesser degree) of execution time and/or simplicity of the final model. In this paper we present a simple filter method for setting attribute weights for use with naive Bayes. Experimental results show that naive Bayes with attribute weights rarely degrades the quality of the model compared to standard naive Bayes and, in many cases, improves it dramatically. The main advantages of this method compared to other approaches for improving naive Bayes is its
run-time complexity and the fact that it maintains the simplicity of the final model
Interpretable Aircraft Engine Diagnostic via Expert Indicator Aggregation
Detecting early signs of failures (anomalies) in complex systems is one of
the main goal of preventive maintenance. It allows in particular to avoid
actual failures by (re)scheduling maintenance operations in a way that
optimizes maintenance costs. Aircraft engine health monitoring is one
representative example of a field in which anomaly detection is crucial.
Manufacturers collect large amount of engine related data during flights which
are used, among other applications, to detect anomalies. This article
introduces and studies a generic methodology that allows one to build automatic
early signs of anomaly detection in a way that builds upon human expertise and
that remains understandable by human operators who make the final maintenance
decision. The main idea of the method is to generate a very large number of
binary indicators based on parametric anomaly scores designed by experts,
complemented by simple aggregations of those scores. A feature selection method
is used to keep only the most discriminant indicators which are used as inputs
of a Naive Bayes classifier. This give an interpretable classifier based on
interpretable anomaly detectors whose parameters have been optimized indirectly
by the selection process. The proposed methodology is evaluated on simulated
data designed to reproduce some of the anomaly types observed in real world
engines.Comment: arXiv admin note: substantial text overlap with arXiv:1408.6214,
arXiv:1409.4747, arXiv:1407.088
Exchangeable Variable Models
A sequence of random variables is exchangeable if its joint distribution is
invariant under variable permutations. We introduce exchangeable variable
models (EVMs) as a novel class of probabilistic models whose basic building
blocks are partially exchangeable sequences, a generalization of exchangeable
sequences. We prove that a family of tractable EVMs is optimal under zero-one
loss for a large class of functions, including parity and threshold functions,
and strictly subsumes existing tractable independence-based model families.
Extensive experiments show that EVMs outperform state of the art classifiers
such as SVMs and probabilistic models which are solely based on independence
assumptions.Comment: ICML 201
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