Extracting informative images from web news pages via imbalanced classification

ABSTRACT

In this paper we propose an imbalanced classification algorithm to extract informative images from web news pages. Our algorithm resolve the difficult problem based on two approaches. First, we limit our dataset to a specific application area so that the patterns of the informative images can be captured by existing classification algorithms. Second, we propose an automatic negative samples filtering algorithm to eliminate most negative samples, so that the classification training data is rebalanced. Because most classification algorithms have reduced performance on imbalanced training data, our algorithm improves the overall performance significantly. In addition, our approach is inherently robust to new web sites and style/layout change of web sites.

REFERENCES

• 1: Alberto H. F. Laender, Berthier A. Ribeiro-Neto, Altigran S. da Silva, and Juliana S. Teixeira. A brief survey of web data extraction tools. ACM SIGMOD Record, 31(2):84--93, 2002.
• 2: Albert Orriols and Ester Bernad'oMansilla. The class imbalance problem in learning classifier systems: A preliminary study. GECCO Workshops, pages 74--78, 2005.
• 3: Yanhong Zhai and Bing Liu. Structured data extraction from the web based on partial tree alignment. IEEE Transactions on Knowledge and Data Engineering, 18(12):1614--1628, 2006.

INDEX TERMS

Primary Classification:
  I. Computing Methodologies
  I.2 ARTIFICIAL INTELLIGENCE
      I.2.6 Learning
          Subjects: Concept learning

Additional Classification:
  H. Information Systems
  H.2 DATABASE MANAGEMENT
      H.2.8 Database applications
          Subjects: Data mining

General Terms:
Algorithms, Experimentation

Keywords:
imbalanced classification, informative image