Archive for the ‘results’ Category

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ECML-PKDD talk on Bayesian network classifiers

September 14, 2018

On the 14th September 2018 I presented the following paper at ECML-PKDD in Dublin.  The slides for the talk are here.

We figured out how to do good smoothing of Bayesian network classifiers.  The same technique works for decision trees, and in fact beats all known algorithms for smoothing/pruning!

“Accurate parameter estimation for Bayesian network classifiers using hierarchical Dirichlet processes”, by François Petitjean, Wray Buntine, Geoffrey I. Webb and Nayyar Zaidi, in Machine Learning, 18th May 2018, DOI 10.1007/s10994-018-5718-0.  Available online at Springer Link.  Presented at ECML-PKDD 2018 in Dublin in September, 2018.

 

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A small model of ArXiv abstracts

April 12, 2016

I’ve been working with Dorota Glowacka of University of Helsinki on a search system built on the ArXiv.org.  We have a demo appearing in SIGIR 2016.  Here is a model with 100 topics built using normalised gamma priors on topics (giving each topic a variance parameter as well as a mean parameter) on the 1.1 million abstracts to March 2016.  The model took about 6 hours to run on my desktop.

This is a huge PNG file (2.8Mb).  YOU will not be able to view it unless you:

  • so load up on a big screen,
  • click on the image to enter image view mode,
  • then scroll down to bottom right click on “View full size” to bring it up,
  • and then zoom around to view.
arXiv.org abstracts to 03/2016

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Visualising a topic model

March 25, 2016

Finally decided to write a proper visualiser for topic models.   I used the WordCloud Python tool from AMueller[GitHub].   Modified it because the input I needed to use words with precomputed scores, rather than text input.  Moreover, I wanted two dimensions for words displayed, size (word frequency in topic) and lightness (degree to which the word is characterised by the topic, measured as frequency over document frequency).  I also scale the final tag cloud depending on the size of the topic in the corpus.  The correlation between topics is computed from the document-topic proportions.  All these then go into GraphViz, where nodes are displayed as images and a lot of careful weighting and organising of the number of topic correlations to display, edge weights, etc.

Below are the results on  ABC news articles from their website 2003-2012 collected by Dr. Jinjing Li of NATSEM in Canberra.   These images are about 4000 by 4000 pixels.  YOU will not be able to view it unless you:

  • get on a big screen,
  • click on the image to enter image view mode,
  • then scroll down to bottom right, click on “View full size” to bring it up,
  • and then zoom around to view.
Banking in the Australian news (from ABC website 2003-2012).
Obesity in the Australian news (from ABC website 2003-2012).

To produce the banking one, I do the following commands with hca:

#  generate the topic model into result set B1
hca -Ang -v -K50 -C1000 -q2 bank B1
#  compute the diagnostics
hca -v -v -V -V -r0 -C0 bank B1
#  generate the image
topset2word.pl --dot "-Kfdp" --lang png  B1 BN1

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