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Kdd Cup 2013 Author Paper Identification Final Report

Kdd Cup 2013 Author Paper Identification Final Report. Ben Deng – M10112006. Outline. Problem Description Database Analysis Research Issue Proposed Ideas Results. Problem Description. Inside the research community, it has more than 50 million publications and 19 million authors.

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Kdd Cup 2013 Author Paper Identification Final Report

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  1. Kdd Cup 2013Author Paper IdentificationFinal Report Ben Deng – M10112006

  2. Outline • Problem Description • Database Analysis • Research Issue • Proposed Ideas • Results

  3. Problem Description Inside the research community, it has more than 50 million publications and 19 million authors. However every Journal, Letter, Conference… have their own format. This include author names. In addition, these formats can lead an author-name ambiguity. For instance, abbreviations, identical names, name misspellings, pseudonyms. All these problems can result in incorrect assign to various authors, and it is enormous problem when we want to search for specific author. The main goal is how to recognize the author and correctly assign the publications to them.

  4. Database Analysis

  5. Research Issue • Lot of data are missing • Noise in affiliation column (especially with foreign affiliation) • Name ambiguity (especially name with chinese origin) • Authors have different abbreviations from different Journals and/or Conference

  6. Proposed Ideas • Filling missing data. • Counting how many different affiliations the same author has. • Using keywords, how many times the same keyword was used. • Class weight is fixed to be auto.

  7. Filling missing data SQL Code UPDATE table t SET city = c.column2 FROM (SELECT column1, MAX(column2) AS column2 FROM table WHERE column2 IS NOT NULL GROUP BY column1) c WHERE t.column2 IS NULL AND column1= c.column1; • In order to normalize the tables such that a one to one join table was created between them which joins each column1 to a single column2, if indeed there should be exactly one column2 per column1.

  8. Simulation and Results

  9. Simulation and Results • Random Forest (Classifier) • Gradient Boosting (Classifier) • Decision Tree (Classifier) • K Nearest (Classifier)

  10. Random Forest • Result is 0.51341, however I am expecting for 0.80217 Using the same code from Github (same parameters)

  11. Random Forest • Result is 0.52469 Parameters of Python Code RandomForestClassifier (n_estimators=200, criterion='gini', max_depth=None, min_samples_split=15, min_samples_leaf=1, min_density=0.10000000000000001, max_features='auto', bootstrap=True, compute_importances=False, oob_score=False, n_jobs=2, random_state=None, verbose=0)

  12. Decision Tree • Result is 0.47386 Parameters of Python Code DecisionTreeClassifier (criterion='gini',  max_depth=None,  min_samples_split=15, min_samples_leaf=1,  min_density=0.10000000000000001, max_features=‘auto’, compute_importances=False, random_state=1)

  13. Gradient Boosting • Result is 0.53506 Parameters of Python Code GradientBoostingClassifier (loss='deviance',  learning_rate=0.00001, n_estimators=250, subsample=0.5, min_samples_split=2,  min_samples_leaf=1,  max_depth=10, init=None, random_state=None,  max_features=None,  verbose=0

  14. K Nearest • Result is 0.48297 Parameters of Python Code KNeighborsClassifier (n_neighbors=50, weights=‘distance', algorithm='auto', l eaf_size=30,  p=2)

  15. SVM SVC, Nu-SVC, LinearSVC • Support Vector Machine (SVC, Nu-SVC and LinearSVC) were tested. However the training was taking more than 3 days and they are still training the classifier. So, I did not be able to finish the training and submit the results using SVM.

  16. Thank You

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