A Comparative Study of Kernel Methods for Classification Applications Yan Liu Sep 23, 2003

Introduction Support Vector Machines Various kernels Text classification Protein classification Various kernels Standard kernels Linear kernels, polynomial kernels, RBF kernels Other application-oriented kernels Fisher-kernels, String kernels and etc

Problem Definition There has been little study focusing on the behaviors of different kernels for: Rare-class problem (unbalanced data) Noisy data problem Multi-label problem These problems are common in the real applications: Text classification Protein Family classification

Text Classification Kernel selection Problem Focus Dataset Linear kernels String kernels Problem Focus Rare-class problem Multi-class problem Dataset Reuters21578 dataset

Protein Family Classification Kernel selection Linear kernels String kernels Fisher-kernels Problem Focus Rare-class problem Noisy data problem Dataset GPCR classification dataset

Methodology and Schedule Propose conjectures on the possible behaviors according to analysis Sep 12th ~ Sep 28th Work on synthetic datasets to testify hypothesis Sep 28th ~ Oct 20th Map from synthetic data to real application data Oct 20th ~ Sep 18th

Mid-course Deliverables Analysis of the dataset Class distribution (rare-class and multi-class) Noise level Conjectures for possible behaviors Results on synthetic datasets Explanation and interesting observations from the results

Multi-label Problem for Text Classification Related work Binary classification (one-vs-all) (by Yang; Joachims) Mixture Model by EM (by McCallum) Rank-based approach Boosting (by Schapire & Singer) Rank-based kernels (by Elsseeff & Weston)

Multi-label Problem for Text Classification Possible Solutions Combine Mixture Model and Kernel-based approach using Fisher-kernels Similar idea as using HMM and SVM together for protein classification