Modeling Unreliable Data and Sensors: Using F-measure Attribute Performance with Test Samples from Low-Cost Sensors

@article{Iyer2011ModelingUD,
  title={Modeling Unreliable Data and Sensors: Using F-measure Attribute Performance with Test Samples from Low-Cost Sensors},
  author={Vasanth Iyer and S. Sitharama Iyengar},
  journal={2011 IEEE 11th International Conference on Data Mining Workshops},
  year={2011},
  pages={15-22}
}
Building a high performance classifier requires training with labeled data, which is supervised and allows generalizing the classifier's decision boundary and in practice most of the data is unlabeled, newer algorithms needs to be learn by knowledge discovery. Sufficient training data are collected in the form of empirical evidence, which have labeled positive and negative samples to build the hypothesis. The hypothesis is constructed by the conjunction of the attributes, which can be learnt by… CONTINUE READING

Figures, Tables, Results, and Topics from this paper.

Key Quantitative Results

  • We show that training accuracy of the small forest fire classifier using attributes from manual logs is enhanced by 30% by using sensor data. The rare and hard to classify large forest fires are 95% accurately classified by using the new Fire Weather Index (FWI).

References

Publications referenced by this paper.
SHOWING 1-10 OF 17 REFERENCES

Fuzzy sets, Information

L. Zadeh
  • Control 8,
  • 1965
VIEW 5 EXCERPTS
HIGHLY INFLUENTIAL

Pattern Recognition and Machine Learning

Christopher M. Bishop
  • 2006
VIEW 1 EXCERPT

Data mining - practical machine learning tools and techniques, Second Edition

  • The Morgan Kaufmann series in data management systems
  • 2005
VIEW 2 EXCERPTS

Witten and Eibe Frank . Data Mining , Pratical machine learning

H Ian
  • 2005