Kaggle LSHTC4 Winning Solution

05/03/2014
by   Antti Puurula, et al.
0

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. Scores for documents are predicted by weighted voting of base-classifier outputs with a variant of Feature-Weighted Linear Stacking. The number of documents per label is chosen using label priors and thresholding of vote scores. This document describes the models and software used to build our solution. Reproducing the results for our solution can be done by running the scripts included in the Kaggle package. A package omitting precomputed result files is also distributed. All code is open source, released under GNU GPL 2.0, and GPL 3.0 for Weka and Meka dependencies.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
09/24/2017

HDLTex: Hierarchical Deep Learning for Text Classification

The continually increasing number of documents produced each year necess...
research
08/15/2020

Label-Wise Document Pre-Training for Multi-Label Text Classification

A major challenge of multi-label text classification (MLTC) is to stimul...
research
02/15/2021

MATCH: Metadata-Aware Text Classification in A Large Hierarchy

Multi-label text classification refers to the problem of assigning each ...
research
09/20/2015

Early text classification: a Naive solution

Text classification is a widely studied problem, and it can be considere...
research
01/31/2019

Funnelling: A New Ensemble Method for Heterogeneous Transfer Learning and its Application to Polylingual Text Classification

Polylingual Text Classification (PLC) consists of automatically classify...
research
01/31/2019

Funnelling: A New Ensemble Method for Heterogeneous Transfer Learning and its Application to Cross-Lingual Text Classification

Cross-lingual Text Classification (CLC) consists of automatically classi...

Please sign up or login with your details

Forgot password? Click here to reset