Project 1: Text Classification by Neural Networks Ver 1.1

(C) 2006, SNU Biointelligence Laboratory Outline Classification using ANN Learn and classify text documents Estimate several statistics on the dataset (C) 2006, SNU Biointelligence Laboratory

(C) 2006, SNU Biointelligence Laboratory Network Structure … Class 1 Class 3 Class 2 Input (C) 2006, SNU Biointelligence Laboratory

CLASSIC3 Dataset

(C) 2006, SNU Biointelligence Laboratory CLASSIC3 Three categories: 3891 documents CISI: 1,460 document abstracts on information retrieval from Institute of Scientific Information. CRAN: 1,398 document abstracts on Aeronautics from Cranfield Institute of Technology. MED: 1,033 biomedical abstracts from MEDLINE. (C) 2006, SNU Biointelligence Laboratory

Text Presentation in Vector Space 문서집합 stemming stop-words elimination feature selection . . . VSM representation 1 2 3 1 Term vectors 1 2 Bag-of-Words representation d1 d2 d3 dn baseball 1 2 3 specs graphics hockey Term-document matrix unix Dataset Format space (C) 2006, SNU Biointelligence Laboratory

Dimensionality Reduction term (or feature) vectors individual feature Scoring measure (on individual feature) Sort by score scores choose terms with higher values documents in vector space ML algorithm Term Weighting TF or TF x IDF TF: term frequency IDF: Inverse Document Frequency N: Number of documents ni: number of documents that contain the j-th word (C) 2006, SNU Biointelligence Laboratory

Construction of Document Vectors Controlled vocabulary Stopwords are removed Stemming is used. Words of which document frequency is less than 5 is removed.  Term size: 3,850 A document is represented with a 3,850-dimensional vector of which elements are the frequency of words. Words are sorted according to their values of information gain.  Top 100 terms are selected  3,830 (examples) x 100 (terms) matrix (C) 2006, SNU Biointelligence Laboratory

Experimental Results

Data Setting for the Experiments Basically, training and test set are given. Training : 2,683 examples Test : 1,147 examples N-fold cross-validation (Optional) Dataset is divided into N subsets. The holdout method is repeated N times. Each time, one of the N subsets is used as the test set and the other (N-1) subsets are put together to form a training set. The average performance across all N trials is computed. (C) 2006, SNU Biointelligence Laboratory

(C) 2006, SNU Biointelligence Laboratory Number of Epochs (C) 2006, SNU Biointelligence Laboratory

(C) 2006, SNU Biointelligence Laboratory Number of Hidden Units Number of Hidden Units Minimum 10 runs for each setting # Hidden Units Train Test Average  SD Best Worst Setting 1 Setting 2 Setting 3  (C) 2006, SNU Biointelligence Laboratory

(C) 2006, SNU Biointelligence Laboratory

Other Methods/Parameters Normalization method for input vectors Class decision policy Learning rates …. (C) 2006, SNU Biointelligence Laboratory

(C) 2006, SNU Biointelligence Laboratory ANN Sources Source codes Free software  Weka NN libraries (C, C++, JAVA, …) MATLAB tool box Web sites http://www.cs.waikato.ac.nz/~ml/weka/ http://www.faqs.org/faqs/ai-faq/neural-nets/part5/ (C) 2006, SNU Biointelligence Laboratory

(C) 2006, SNU Biointelligence Laboratory Submission Due date: October 12 (Thur) Both ‘hardcopy’ and ‘email’ Used software and running environments Experimental results with various parameter settings Analysis and explanation about the results in your own way FYI, it is not important to achieve the best performance (C) 2006, SNU Biointelligence Laboratory