A Practical Approach to Novel Class Discovery in Tabular Data: Full training procedure
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Authors:
(1) Troisemaine Colin, Department of Computer Science, IMT Atlantique, Brest, France., and Orange Labs, Lannion, France;
(2) Reiffers-Masson Alexandre, Department of Computer Science, IMT Atlantique, Brest, France.;
(3) Gosselin Stephane, Orange Labs, Lannion, France;
(4) Lemaire Vincent, Orange Labs, Lannion, France;
(5) Vaton Sandrine, Department of Computer Science, IMT Atlantique, Brest, France.
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Table of Links
Abstract and Intro
Related work
Approaches
Hyperparameter optimization
Estimating the number of novel classes
Full training procedure
Experiments
Conclusion
Declarations
References
Appendix A: Additional result metrics
Appendix B: Hyperparameters
Appendix C: Cluster Validity Indices numerical results
Appendix D: NCD k-means centroids convergence study
6 Full training procedure
In the previous sections, we presented the models, the hyperparameter optimization and the estimation procedure of the number of novel classes independently. In this section, these components are brought together to form a complete training procedure. To ensure that no prior knowledge about the novel classes is ever used in this process, the number of novel classes is naturally estimated during the k-fold CV introduced in Section 4. As the whole process is quite complex, we try to summarize it in clear terms in this section and in Algorithm 1.
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This paper is available on arxiv under CC 4.0 license.
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