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On classifier behavior in the presence of mislabeling noise

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Abstract

Machine learning algorithms perform differently in settings with varying levels of training set mislabeling noise. Therefore, the choice of the right algorithm for a particular learning problem is crucial. The contribution of this paper is towards two, dual problems: first, comparing algorithm behavior; and second, choosing learning algorithms for noisy settings. We present the “sigmoid rule” framework, which can be used to choose the most appropriate learning algorithm depending on the properties of noise in a classification problem. The framework uses an existing model of the expected performance of learning algorithms as a sigmoid function of the signal-to-noise ratio in the training instances. We study the characteristics of the sigmoid function using five representative non-sequential classifiers, namely, Naïve Bayes, kNN, SVM, a decision tree classifier, and a rule-based classifier, and three widely used sequential classifiers based on hidden Markov models, conditional random fields and recursive neural networks. Based on the sigmoid parameters we define a set of intuitive criteria that are useful for comparing the behavior of learning algorithms in the presence of noise. Furthermore, we show that there is a connection between these parameters and the characteristics of the underlying dataset, showing that we can estimate an expected performance over a dataset regardless of the underlying algorithm. The framework is applicable to concept drift scenarios, including modeling user behavior over time, and mining of noisy time series of evolving nature.

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Notes

  1. When we write performance of an algorithm, we mean the classification accuracy.

  2. Throughout the rest of this work we will use the term noise to refer to the label noise, unless otherwise indicated.

  3. A preliminary version of this work has appeared in Mirylenka et al. (2012).

  4. Instead of 0.05, one can use any value close to 0, describing a normalized measure of distance from the optimal performance. In the case of \(p = 0.05\), the distance from the optimal performance is \(5\%\).

  5. We note that high levels of noise such as 95% are often observed in the presence of concept drift, e.g., when learning computer-user browsing habits in a network environment with a single IP, and several different users sharing it.

  6. http://stat.ethz.ch/R-manual/R-patched/library/nlme/html/ACF.html

  7. Repeated random sub-sampling validation is also known as Monte Carlo cross-validation (Kuhn and Johnson 2013).

  8. The settings of the genetic algorithm can be found in the appendix.

  9. We would like to thank Christos Faloutsos for kindly providing the code for the fractal dimensionality estimation.

  10. RMAE is also known as mean absolute percentage error.

  11. See http://jgap.sourceforge.net/.

  12. See http://www.jsc.nildram.co.uk/.

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Acknowledgements

We thank the editor and the reviewers of this paper for their valuable comments and advice. K. Mirylenka also thanks Daniil Mirylenka for proofreading the paper.

Author information

Authors and Affiliations

  1. IBM Research–Zurich, Säumerstrasse 4, 8803, Rüschlikon, Switzerland

    Katsiaryna Mirylenka

  2. Institute of Informatics and Telecommunications of NCSR Demokritos, Ag. Paraskevi, 15310, Athens, Greece

    George Giannakopoulos

  3. University of Trento, via Sommarive 5, 39123, Trento, Italy

    Le Minh Do

  4. Department of Computer Science, Paris Descartes University, 45 Rue Des Saints-Peres, 75006, Paris, France

    Themis Palpanas

Authors
  1. Katsiaryna Mirylenka
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  2. George Giannakopoulos
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  3. Le Minh Do
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  4. Themis Palpanas
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Correspondence to Katsiaryna Mirylenka.

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Responsible editor: Johannes Fuernkranz.

Katsiaryna Mirylenka: Work mainly done while at the University of Trento, Italy.

Appendices

Appendices

1.1 Appendix 1: The genetic algorithm settings

We used the JGAPFootnote 11 genetic algorithms package for the search in the sigmoid parameter space and Java Statistical Classes library for the statistical measurement the Kolmogorov–Smirnov (KS) test.Footnote 12 Default operators for double numbers were used. The alleles were five parameters, one per parameter:

  • m with allowed values in [0.0, 0.5].

  • M with allowed values in [0.5, 1.0].

  • b with allowed values in [0.0, 50.0].

  • c with allowed values in [0.0, 50.0].

  • d with allowed values in \([- 5.0, 5.0]\).

Essentially, employing the genetic algorithm, we try to maximize the following quantity:

$$\begin{aligned} fitness(i) = 100 * \left( 1 + \frac{1}{D+1}\right) , \end{aligned}$$

where i is a candidate individual in the genetic algorithm, corresponding to a given set of parameter values and fitness(i) the value of the fitness function for that individual. The parameter D is the D statistic of the KS test (Marsaglia et al. 2003), which is higher when the fit is worse. The population per iteration is 10,000 individuals. Our search ends when there is no significant (that is \(10^{-5}\)) improvement after 20 consecutive iterations of the genetic algorithms, or when 1000 iterations have been completed.

1.2 Appendix 2: Correlation results

Non-sequential case

All the correlation coefficients with the corresponding p-values are shown in Table 11. Green (dark) cells mark the pairs that have medium correlation. P-values of importance are emphasized as follows: (p-value <0.05 underlined bold cells, p-value <0.1 italics-bold cells).

Table 11 The values of three correlation coefficients between parameters of the dataset and the parameters of the sigmoid non-sequential case (Color table online)
Full size table

Sequential case

All the correlation coefficients with the corresponding p-values are shown in Table 12, where yellow (light) cells mark the pairs that have strong statistically significant correlation, green (dark) cells mark the pairs that have medium statistically significant correlation. If p-value <0.05 the cell is emphasized with underlined bold, if p-value <0.1—with italics-bold.

Table 12 Three correlation coefficients between parameters of the dataset and parameters of the sigmoid sequential case (Color table online)
Full size table

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Mirylenka, K., Giannakopoulos, G., Do, L.M. et al. On classifier behavior in the presence of mislabeling noise. Data Min Knowl Disc 31, 661–701 (2017). https://doi.org/10.1007/s10618-016-0484-8

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