Multi-class SVM with Negative Data Selection for Web Page

Classification

Chih-Ming Chen, Hahn-Ming Lee and Ming-Tyan Kao

International Joint Conference on Neural Networks 2004

Motivation

• Several new websites are launched everyday

• Need to search fast and efficiently

• Search engines organize websites under topic hierarchy (taxonomy)

• Need a classifier: one-against-all SVM

• Catch: huge negative data increased training time

Negative Data Selection

Support vectors in the negative data are much similar to thepositive data than the other negative data

Negative Data Selection

1. Feature Selection: top n keywords from the positive data

2. All websites are represented as vectors of these top n keywords.

3. Cosine Similarity:

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Negative Data Selection

• Plot similarity scores of negative to positive documents in descending order with negative documents

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Negative Documents

Convergence Point

Experiments

• Reuters dataset (10802 training, 565 test)

Class Number of Positive Data

Number of Negative Data

Crude 580 10222

Trade 475 10327

Dlr 162 10640

Nat-gas 92 10710

Acq 2357 8445

Experiments

Experiments