Abstraction, aggregation and recursion for generating accurate and simple classifiers

@inproceedings{Honavar2006AbstractionAA,
  title={Abstraction, aggregation and recursion for generating accurate and simple classifiers},
  author={Vasant G. Honavar and Dae-Ki Kang},
  year={2006}
}
An important goal of inductive learning is to generate accurate and compact classifiers from data. In a typical inductive learning scenario, instances in a data set are simply represented as ordered tuples of attribute values. In our research, we explore three methodologies to improve the accuracy and compactness of the classifiers: abstraction, aggregation, and recursion. Firstly, abstraction is aimed at the design and analysis of algorithms that generate and deal with taxonomies for the… CONTINUE READING

Topics from this paper.

References

Publications referenced by this paper.
SHOWING 1-10 OF 131 REFERENCES

AVT-NBL: an algorithm for learning compact and accurate naive Bayes classifiers from attribute value taxonomies and data

  • Fourth IEEE International Conference on Data Mining (ICDM'04)
  • 2004
VIEW 14 EXCERPTS
HIGHLY INFLUENTIAL

Results of the DARPA 1998 Offline Intrusion Detection Evaluation

  • Recent Advances in Intrusion Detection
  • 1999
VIEW 6 EXCERPTS
HIGHLY INFLUENTIAL

Bayesian Network Classifiers

VIEW 14 EXCERPTS
HIGHLY INFLUENTIAL

Similar Papers

Loading similar papers…