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Results and Comparison of VSM Model and NB Model for Document Classification

This project presents the results and comparison of the Vector Space Model (VSM) and Naive Bayes (NB) model for document classification. The project analyzes the performance of both models and discusses the improvements made in the VSM model. The project also highlights the challenges faced in applying VSM to specific domains, such as books and cars. Additionally, the project demonstrates the effectiveness of VSM in clustering similar data items. Overall, both models are found to be efficient, with VSM being easier to implement and faster. However, NB model requires more training data, particularly negative examples.

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Results and Comparison of VSM Model and NB Model for Document Classification

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  1. Project 2 CS652

  2. Project2 Presented by: REEMA AL-KAMHA

  3. Results • VSM model: • The training set contains 18 documents (10 positive, 8 negative) . • Ontovector(1 , 0.91 , 1 , 0.85 , 0.90 , 1.16 , 0.33) corresponds to (Type, GoldAlloy , Price, Diamondweight, MetalKind, Jem,JemShap) • Threshold=0.7 • Testing set contains 20 documents (10 positive, 10 negative) • Recall=1 , Presion=0.8 • Testing set for instructor 24 documents (1positive, 23 negative) • Recall=1 , Presion=1

  4. Results • NB Model: • The training set contains 18 documents (10 positive, 8 negative) . • Testing set contains 20 documents (10 positive, 10 negative) • Recall=1 , Precision=0.7 • Testing set for instructor 24 documents (1positive, 23 negative) • Recall=1 , Precision=0.5

  5. Comments • VSM Model: • the average of each attribute= the number of occurrence of the attribute/the number of records. • NB Model: • For vocabulary document remove all stop-words. • In the result I always have Recall=1 which means the process does not discard any relevant document.

  6. Muhammed Documents classification • Two ways have been implemented in Java. - VSM– Vector Space Model. - NB – Naive Bayes. • Applying VSM on my domain –Books- was not without problems. The problems basically because of the meaning of the title and the author. For e.g., when trying to apply VSM on cars, there are some thing needed to be figured out such do we consider the model of the car as title and the make as author? Of course such assumption made some troubles since some of irrelevant documents became relevant.

  7. So the philosophy was to ignore the title and author and use the other attributes to judge if the document is relevant or not. You can see from the table that the car almost about to attain the threshold. Recall =100% Note: when we take the title and the author Precision=100% into consideration the threshold Threshold= 76% becomes 0.999.

  8. The threshold for Books domain • Other documents similarity: • Drug = 0.435 • Real_estate=0.599 • Computer=0.423

  9. Naïve Bayes

  10. Conclusion • Both of the implemented ways are efficient. • VSM is easier to implement and faster. • Much time spent because I misunderstood NB algorithm– this was my problem. • When amplifying some key attributes that is almost unique to a domain, 100% precision and recall is very possible. • NB is not very sensitive to the boundary values.

  11. Tim Chartrand Project 2 Results • Application Domain: • Software (Shareware and Freeware) • Size of training set • Positive: 10 • Negative: 10

  12. VSM Improvements • Normalize positive training example results to find the per record expected values • Add a weight to each attribute: • Epos(i) = expected value for attribute i in positive examples • Eneg(i) = expected value for attribute i in negative examples • Diff(i) = Epos(i) - Eneg(i) • Weight(i) =Diff(i) / maxj=1..n Diff(j) • Ontology(i) = Epos(i) * Weight(i) • Weighting results: • Average difference improved from 0.587 to 0.714 • Separation improved from 0.280 to 0.422 • Price given a weight of 0 – not considered in document classification

  13. Bayes Results & Improvements • Improvements – Reduce vocabulary to “best” words: • Eliminate stopwords • Stemm common prefixes and suffixes • Ignore case • Eliminate numbers • Remove non-alphabetic characters before and after a word *** Somewhat artificial result. I started out at about 10% Precision and added negative training examples until I correctly classified all test examples.

  14. VSM Vs. Bayes • End results were the same, but … • VSM performed better using only the original dataset • Bayes seems to need more training data (mainly negative) • Major advantage of VSM – Clustering: • Using the ontology as a vector allowed effective clustering of similar data items (i.e. dates, prices, etc) • Reduced dimensionality from about 1500 to 8

  15. Text Classification Helen Chen CS652 Project 2 May 31, 2002

  16. Documents and Methods • Application: movie • Documents: • Training Set: • Positive Docs: 5; Negative Docs: 24 • My Test Set: • Positive Docs: 5; Negative Docs: 14 • Methods: VSM model and NB

  17. VSM: threshold is 0.65 NB Vector Space Model and Naïve Bayes Results tested on my own testing set and instructor-provided testing set (23 negative docs, 1 positive docs) for VSM model (left) and NB (right)

  18. Comments on VSM • Weighting is critical to performance • Assign weight according to positive examples • Adjust weight according to negative examples Weights assigned on each attribute

  19. Comments on NB • The choice of irrelevant documents in training set is critical to the performance Results for “Clustered” training set Results for evenly distributed training set

  20. Yihong’s Project2 • Target topic: Apartment Rental • Training Sets • 5 positive, 10 negative • Testing Sets • Self sets: 5 positive, 9 negative • TA sets: 1 positive, 23 negative

  21. VSM Results • 100% Precision and Recall for both self-collected sets and TA-collected sets • Threshold Value: 0.868 • Most similar application • Real Estate, range: 0.792~0.846 • Compare with AptRental, range: 0.891~0.937 • Weighting attributes • Precision-weighted • Recall-weighted • F-measure-weighted

  22. Naïve Bayes Results • 100% Precision and Recall for both self-collected sets and TA-collected sets • Summation instead of production • to avoid the problem of underflow

  23. More Comments • Machine cannot know what is unknown • training examples must be representative • Estimate of prior probability of target values is very important • 50% estimate to 4.2% “real” distribution is undesired, precision is 25% • 33% estimate, achieve 100%, over 50% irrelevant cases pos – neg < 3 • Cluster special attributes, like phone number, price, etc. (similar thinking as our ontology) • Distributional clustering • should work fine because of low noisy level for semi-structural documents

  24. David Marble CS 652 Spring 2002 Project 2 – VSM/Bayes

  25. Results (My Test Data) RECALLPRECISION VSM 8/10 10/10 80% 100% Failed on: Classified ads, car ads with a lot of info. Bayes 9/10 10/10 90% 100% Failed on: Missed one restaurant page! That page had no food description and city names from outside my training set. FoodType was the key. (Not too many extraneous documents have the words “mexican, fish, BBQ, chinese,” etc. These words show up on average just over 2 times per record in the positive training documents.)

  26. Results (BYU Data) RECALLPRECISION VSM 20/24 24/24 83% 100% Failed on: Cars, Apartments, Shopping and Real Estate. Lots of phone #’s, addresses, cities and states – a name is a given (how can you distinguish what a restaurant name is? Bayes 24/24 24/24 100% 100% Failed on: Nothing. Once again, FoodType was the key. Luckily, the one applicable document had food type listed.

  27. Comments • Training data contained State & Zip only half the time. • Names of restaurants could not be a specific term, therefore just about every record had a “restaurant name.” • Mainly did well with Naïve Bayes because of FoodType extraction – average of over 2 per record in training data and covered most of the possible food terms.

  28. VSM and Bayes search results Lars Olson

  29. My Test Data • 5 positive, 6 negative (including obituaries) • VSM: • Using 83% threshold: • Precision: 4/5 = 80% • Recall: 4/5 = 80% • Using 80% threshold: (accepts one training doc incorrectly) • Precision: 5/6 = 83.3% • Recall: 5/5 = 100% • Bayes: • Precision: 5/5 = 100% • Recall: 5/5 = 100%

  30. TA Test Data • 1 positive, 23 negative (including obituaries) • VSM: • Precision: 1/2 = 50% • Recall: 1/1 = 100% • Bayes: • Precision: 1/1 = 100% • Recall: 1/1 = 100%

  31. Comments • Obituaries vs. genealogy data? • Rejected by Bayes, but obituary examples in training set could affect that • Changes VSM to 100% precision and recall for both test sets at 80% threshold (although one training doc is still accepted incorrectly) • Incomplete lexicons • High variance (Gender: 0.7% to 100%, Place: 0% to 84.3% in training documents) • Zero vector undefined in VSM

  32. Craig Parker

  33. My Results • VSM • cut-off value 0.85 • 100% correct • Bayesian • Classified everything as a non-drug

  34. DEG Results • VSM • 100 % Correct using predetermined cutoff value of 0.85 (I think) • Bayesian • Identified everything as negative (although the margin was smaller on drug than on non-drugs)

  35. Comments • VSM worked very well for drugs. • Would have been even better with a cleaner dictionary of drug names. • Dose and Form were the most important distinguishers • Something wrong with my Bayesian calcuations

  36. Project 2 - Radio Controlled Cars Jeff Roth

  37. Results - My Tests

  38. Comments • Digital Camera always positive, even out scored RC Car adds on VSM - lots of matches on battery and charger • Both algorithms had trouble with very unrelated documents - docs where almost no term matches found • Naïve Bayes had most trouble when test set wasn’t similar to RCCars or any of the documents used in the training set • Combining VSM with NB using a logical AND was very successful

  39. VSM results • Weight i= n+i/ N+ - n-i/ N- • Weight i= n+i/ N+ • Threshold = avg(sim(+)) – avg(sim(-)) ~ 0.61

  40. Traditional VSM vs. Onto. VSM • Consider not only attributes, but values • Achieve keyword clustering • Find a way that can automatically and efficiently define the query words

  41. Naïve Bayes Results Bayes: • Requires relevant large number of training set, especially for the (-) Set • Requires good distribution of the training set • Improvement: • Eliminate Stopwords (obtained from: http://www.oac.cdlib.org/help/stopwords.html • Ignore case

  42. Conclusion • Both work fine • Naïve Bayes: More picky to training set, but not depend on the pre-defined keyword or the ontology • VSM: Application dependent, perform better, provide relevance rank

  43. Finding documents about campgrounds Alan Wessman

  44. VSM: Precision: 100% Recall: 100% F-measure: 100% Classification threshold value = 0.660 Naïve Bayes: Precision: 86% Recall: 100% F-measure: 92% Results for My Test Set

  45. VSM: Precision: 20% (1/5) Recall: 100% (1/1) F-measure: 33% Naïve Bayes: Precision: 20% (1/5) Recall: 100% (1/1) F-measure: 33% Results for Class Test Set

  46. Observations • Calculating precision in NB: product of many small probabilities becomes zero • NB: Accuracy affected by number and percentage of tokens found in vocabulary • VSM: Accuracy strongly affected by how similarly the different documents “support” the ontology • VSM: Choosing a higher threshold (0.730) would have given F = 75% for my test set and F = 66% for the class test set

  47. Text ClassificationCS652 Project #2 Yuanqiu (Joe) Zhou

  48. Vector Space Model • Query Vector (based on 34 records) • constructed by a document with 34 records • Brand (1.0), Model (1.0), CCDResolution (1.0), ImageResolution (0.65), OpticalZoom (1.0), DigitalZoom (0.88) • Threshold 0.92 • Obtained by computing the similarities of two relevant documents(0.99, 0.98)and two similar documents(0.74, 0.83) to the query • Document Vectors • Self-collected • 5 positive (> 0.97) and 5 negative (< 0.89) • Recall = 100% and Precision = 100% • TA-proivded • Positive( = 0.99) Negative(< 0.58) • Recall = 100% and Precision = 100%

  49. Naïve Bayes Classifier • Training Set • 20 positive • 28 negative (20 of them very similar) • Testing Set • Self-collected • 10 positive • 15 negative (10 of them very similar) • |Ra| = 8, R = 10, A = 12, • Recall = 80%, Precision = 66% • TA-provided • |Ra| = 1, R = 1, A = 1 • Recall = 100%, Precision = 100%

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