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Meta Learning: For Classification

Meta Learning: For Classification. Daniel Spohn drspohn@student.ysu.edu Youngstown State University 03-23-06. Introduction - Meta-Learning. Process to improve the results by using additional methods.

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Meta Learning: For Classification

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  1. Meta Learning:For Classification Daniel Spohn drspohn@student.ysu.edu Youngstown State University 03-23-06

  2. Introduction - Meta-Learning Process to improve the results by using additional methods. • Bagging, Stacking and Boosting all involve running multiple classification algorithms or running the same algorithm multiple times. • Meta-Learning can also be run on a table to determine which algorithm is most appropriate for Learning with. Daniel Spohn Youngstown State University

  3. Stacking • Using multiple algorithms and combining their results. • Output from the previous layer is passed on as input to the next layer. • Ex. The Output of a decision tree and be used as input for a neural network. • This combining of algorithms helps reduce the problems of an individual algorithm. Daniel Spohn Youngstown State University

  4. Boosting • Algorithm is recursively run until misclassification is low. • The instances that are difficult to classify are given higher weight and the algorithm is run again. • As an alternative, boosting can be run with random sub-samples each time, instead of weights. Daniel Spohn Youngstown State University

  5. Bagging • Bagging (Bootstrap Aggregation) • Implements “Voting” • Multiple algorithms are run independently on different subsets of the dataset. • The results of the algorithms are then combined to create the final model. Daniel Spohn Youngstown State University

  6. Meta-Classifiers • Meta-Learning using Meta-Classifiers • Meta-Classifiers are the attributes of the dataset. (ex. Number of columns, types of attributes, etc.) • Using the Meta-Classifiers a dataset is compared to previously analyzed datasets and a ranking is produced to showing the estimated effectiveness of classification algorithms. Daniel Spohn Youngstown State University

  7. Datasets - Adults • Adult.arff • 48842 instances • 14 attributes (6 continuous, 8 nominal) • Contains information on adults such as age, gender, ethnicity, martial status, education, native country, etc. • The instances are classified into either “Salary >50K” or “Salary <= 50K” Daniel Spohn Youngstown State University

  8. Datasets – Census Income • census-income.arff • 199,523 instances • 40 attributes (7 continuous, 33 nominal) • Demographic information and monetary information from the 1994 and 1995 surveys conducted by the U.S. Census Bureau. • The instances are classified into either “Income >50K” or “Income <= 50K” Daniel Spohn Youngstown State University

  9. Datasets - Census Income • The dataset has already been split into a training set and a testing set (2/3 training, 1/3 testing). • This dataset contains missing values, and some attributes may need to be discretized to improved performance and effectiveness of the algorithm. • Additionally some attributes may need to be removed because of irrelevancy. Daniel Spohn Youngstown State University

  10. Analyzing Adult Dataset • Running this dataset in Weka MetaL (Meta Classifiers) produces: • The Top ranked Algorithm is LogitBoost. Daniel Spohn Youngstown State University

  11. Analyzing Adult Dataset • When using Weka MetaL's top ranked algorithm, LogitBoost, 84.68% of instances are correctly classified. • When using Weka MetaL's lowest ranked algorithm, ZeroR, 76.07% of instances are correctly classified. Daniel Spohn Youngstown State University

  12. Analyzing Census Dataset • Took a sample of 9,953 records (of 199,523) • Of the 40 attributes, selected 9 of the most important to help classification. • Grouped the attributes that contain continuous numbers into static groups. Daniel Spohn Youngstown State University

  13. Analyzing Census Dataset • Running J48 results in: • <=50K = 99%Correctly Classified • >50K = 17% Correctly Classified • Running ADABoost with J48 as the classifier: • <=50K = 98%Correctly Classified • >50K = 36% Correctly Classified Daniel Spohn Youngstown State University

  14. Summary • Meta Learning helps improve results over the basic algorithms. • Using Meta Characteristics on the Adult dataset to determine an appropriate algorithm, I achieved almost 85% correct classification. • Using Boosting – AdaBoost with J48 correctly classified more than twice the amount of correct instances for the second group, than J48 alone. Daniel Spohn Youngstown State University

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