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Ensembles

Ensembles. Ensemble Methods. Construct a set of classifiers from training data Predict class label of previously unseen records by aggregating predictions made by multiple classifiers Why might this be useful?

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Ensembles

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  1. Ensembles

  2. Ensemble Methods • Construct a set of classifiers from training data • Predict class label of previously unseen records by aggregating predictions made by multiple classifiers • Why might this be useful? • In Olympic figure skating competition, why are there multiple judges? Why is this a good idea and do these reasons translate to data mining with ensembles?

  3. General Idea

  4. Why does it work? • Suppose there are 25 base classifiers • Each classifier has error rate,  = 0.35 • Assume classifiers are independent • Probability that the ensemble classifier makes a wrong prediction (first term is C(25,i)) • Practice has shown that even when independence does not hold results are good • But you want some diversity in the classifiers

  5. Methods for generating Multiple Classifiers • Manipulate the training data • Sample the data differently each time • Examples: Bagging and Boosting • Manipulate the input features • Sample the featurres differently each time • Makes especially good sense if there is redundancy • Example: Random Forest • Manipulate Class labels • Convert multi-class problem to ensemble of binary classifiers • Manipulate the learning algorithm • Vary some parameter of the learning algorithm • For example: amount of pruning, ANN network topology, etc.

  6. Background • Classifier performance can be impacted by: • Bias: assumptions made to help with generalization • "Simpler is better" is a bias. But we don’t know what the best bias is, so varying it may help • Variance: a learning method will give different results based on small changes (e.g., in training data). • When I run experiments and use random sampling with repeated runs, I get different results each time. • Noise: measurements may have errors or the class may be inherently probabilistic

  7. How Ensembles Help • Ensemble methods can assist with the bias and variance • Averaging the results over multiple runs will reduce the variance • I observe this when I use 10 runs with random sampling and see that my learning curves are much smoother • Ensemble methods especially helpful for unstable classifier algorithms • Decision trees are unstable since small changes in the training data can greatly impact the structure of the learned decision tree • If you combine different classifier methods into an ensemble, then you are using methods with different biases • You are more likely to use a classifier with a bias that is a good match for the problem • You may even be able to identify the best methods and weight them more

  8. Examples of Ensemble Methods • How to generate an ensemble of classifiers? • Bagging • Boosting • These methods have been shown to be quite effective • You can also combine different classifiers built separately • By simple voting • By voting and factoring in the reliability of each classifier • Enterprise Miner allows you to do this by combining diverse classifiers. I believe WEKA permits this also.

  9. Bagging • Sampling with replacement • Build classifier on each bootstrap sample • Each sample has probability (1 – 1/n)n of being selected (about 63% for large n) • Some values will be picked more than once • Combine the resulting classifiers, such as by majority voting • Greatly reduces the variance when compared to a single base classifier

  10. Boosting • An iterative procedure to adaptively change distribution of training data by focusing more on previously misclassified records • Initially, all N records are assigned equal weights • Unlike bagging, weights may change at the end of boosting round

  11. Boosting • Records that are wrongly classified will have their weights increased • Records that are classified correctly will have their weights decreased • Example 4 is hard to classify • Its weight is increased, therefore it is more likely to be chosen again in subsequent rounds

  12. Combining Multiple Models • You can also combine different types of classifiers • For example, a Neural Network and a decision tree model • The simplest manner is to vote or combine the probability estimates associated with the output of each classification decision

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