An information theoretic feature selection framework based on integer programming

  title={An information theoretic feature selection framework based on integer programming},
  author={Siqi Nie and Tian Gao and Qiang Ji},
  journal={2016 23rd International Conference on Pattern Recognition (ICPR)},
We propose a general framework for information theoretic feature selection based on the integer programming. Filter feature selection methods usually rely on a greedy forward or backward selection heuristic to find a satisfactory set of features, as the exact search is a combinatorial problem. We formulate the existing filter information theoretic criteria into an integer programming problem, and by using objective functions, we can represent many different existing scoring criteria. The… CONTINUE READING

From This Paper

Figures, tables, and topics from this paper.


Publications citing this paper.


Publications referenced by this paper.
Showing 1-10 of 23 references

Feature selection based on mutual information

2015 9th International Conference on IT in Asia (CITA) • 2015
View 4 Excerpts
Highly Influenced

A comprehensive evaluation of multicategory classification methods for microarray gene expression cancer diagnosis

Alexander Statnikov, Constantin F Aliferis, Ioannis Tsamardi-nos, Douglas Hardin, Shawn Levy
Bioinformatics • 2005

Efficient Feature Selection via Analysis of Relevance and Redundancy

Journal of Machine Learning Research • 2004
View 1 Excerpt

Similar Papers

Loading similar papers…