Weka solution for the 2004 kdd cup protein homology prediction task
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Weka solution for the 2004 KDD Cup Protein Homology Prediction task. Bernhard Pfahringer Weka Group, University of Waikato, New Zealand. The problem. Detect homologous protein sequences 153 train sequences * ~1000 sequences ==> 145751 pairs classified as match or not

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Weka solution for the 2004 KDD Cup Protein Homology Prediction task

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Weka solution for the 2004 KDD CupProtein Homology Prediction task

Bernhard Pfahringer

Weka Group, University of Waikato, New Zealand


The problem

  • Detect homologous protein sequences

  • 153 train sequences * ~1000 sequences ==>

    145751 pairs classified as match or not

  • Very skewed: only 1296 matches (< 1%!)

  • BUT: excellent attributes


The attributes


Algorithms doing well

  • 2fold cross-validation, looking only at predictive accuracy:

    • Linear SVM (with Logistic model on output for better probs, Platt1999)

    • 10 AdaBoosted unpruned decision trees

    • Random rules (~ RandomForest, ECML2004 Rule learning WS)


Performance criteria

  • Top1: fraction of blocks with a homologous sequence ranked top1 (max)

  • RMSE: root mean squared error (min)

  • RKL: average rank of the lowest ranked homologous sequence (min)

  • APR: average of the average precision in each block (max)

  • Only RMSE depends on absolute values, for all other criteria a good ranking is sufficient


Unique Solution

  • Voted ensemble of three classifiers:

    • Linear SVM + logistic model on output

    • Adaboosted 10 unpruned J48 trees

    • 10^5 random rules

  • Non-standard voting:

    • If SVM and RandomRules agree ==>

      • Average their probabilities

    • ELSE

      • Use Booster as tie-breaker

  • Lucky (first on Proteins, 18th on Physics)


Ensemble performance


Attribute ranks


What I should have done

  • Optimize separately

  • Bagging for better probability estimates

  • More data engineering (e.g. PCA, …)

  • View it as an outlier detection problem

  • Utilize block structure

  • ?


(Standard) Lessons

  • Data engineering (good attributes) essential

  • Ensembles are more robust

  • Weka is not just an educational tool

    • [at least some parts scale well]

  • Java/open source DM tools are competitive

  • But: could improve Weka considerably ( volunteers and/or sponsors, get in touch :-)


Finally

  • A big “THANK YOU” to the organizers of the KDD Cup 2004 !


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