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Feature-weighted linear stacking
Original
Sill, J., Takács, G., Mackey, L., Lin, D.: · 2009
Earlier work this paper cites.
The weka data mining software: An update
Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., Witten, I.H.: · 2009
Earlier work this paper cites.

Similar

Scalable text classification with sparse generative modeling
Puurula, A.: · 2012
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Combining modifications to multinomial naive bayes for text classification
Puurula, A.: · 2012
Cited alongside, same era.

Then

Integrated instance- and class-based generative modeling for text classification
Puurula, A., Myaeng, S.H.: · 2013
Later among the works it cites.
Cumulative progress in language models for information retrieval
Puurula, A.: · 2013
Later among the works it cites.

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2014

Kaggle LSHTC4 Winning Solution

Puurula, Antti, Read, Jesse, Bifet, Albert

Understand

Our winning submission to the 2014 Kaggle competition for Large Scale Hierarchical Text Classification (LSHTC) consists mostly of an ensemble of sparse generative models extending Multinomial Naive Bayes.

The base-classifiers consist of hierarchically smoothed models combining document, label, and hierarchy level Multinomials, with feature pre-processing using variants of TF-IDF and BM25.
Additional diversification is introduced by different types of folds and random search optimization for different measures.
The ensemble algorithm optimizes macroFscore by predicting the documents for each label, instead of the usual prediction of labels per document.