Consolidated Results
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Method (Classifier)ParametersFeaturesCross ValidationTest
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PrecisionRecallPrecisionRecallColorMethod
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BR (KNN)K varying from 3-12. Best resutls with K=8Frequent unigrams and ratings0.7507±0.01110.5305±0.0082Binary classifier for each category
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BR (Naive Bayes)Frequent unigrams and ratings0.6813±0.01420.6740±0.0137Multiclass classifier for each subset of the categories that appear in the data
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BR (SVM)Frequent unigrams, ratings, and hand picked unigrams0.7549±0.00410.6607±0.0106Ensemble of classifiers for a small subset of categories that appear in the data
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BR (Adaboost)Frequent unigrams, ratings, and hand picked unigrams0.7448±0.00680.6173±0.0069KNN algorithm by Zhou et al. which works on Multi-label data
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BR (Bagging using decision trees)Frequent unigrams and ratings0.7259±0.01430.6744±0.0113
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(BR) Neural Network20, 20Frequent unigrams, ratings, and hand picked unigrams0.4865±0.02850.5760±0.0660
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20, 20, 20Frequent unigrams, ratings, and hand picked unigrams0.5052±0.02340.5577±0.0206
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25, 25, 25Frequent unigrams, ratings, and hand picked unigrams0.5132±0.08610.5321±0.1003
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40, 40, 40Frequent unigrams, ratings, and hand picked unigrams0.1978±0.02810.2141±0.0605
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BR (Bagging using decision trees)Frequent unigrams, ratings, hand picked unigrams bigrams0.7330±0.01120.6793±0.0129
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BR (Bagging using decision trees)Frequent unigrams, ratings, and hand picked unigrams0.7259±0.01430.6744±0.0113
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Powerset (Naive Bayes)Frequent unigrams and ratings0.6820±0.00840.6721±0.0098
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Powerset (Naive Bayes)Frequent unigrams, ratings, and hand picked unigrams0.6820±0.00800.6747±0.0082
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Powerset (decision trees)10 decision treesFrequent unigrams and ratings0.7082±0.01560.7010±0.0163
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Powerset (decision trees)10 decision treesFrequent unigrams, ratings, and hand picked unigrams0.7087±0.00860.7024±0.0090
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Powerset (decision trees)10 decision treesFrequent unigrams, ratings, hand picked unigrams bigrams0.7103±0.00600.7060±0.0068
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Ensemble of Classifiers (decision trees)10 decision trees with subset size = 3Frequent unigrams, ratings, and hand picked unigrams0.7201±0.01310.6656±0.0101
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Ensemble of Classifiers (decision trees)10 decision trees with subset size = 3Frequent unigrams, ratings and Frequent bigrams0.7207±0.01050.7057±0.01060.720.71
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AA (MLKNN)K varying from 3-12. Best resutls with K=8Frequent unigrams and ratings0.7124±0.01770.5273±0.0181
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