论文信息 - Supervised learning for the analysis of process operational data

Supervised learning for the analysis of process operational data

Abstract For the extraction of useful knowledge from recorded process operational data, several data mining algorithms are examined on a data set generated by a dynamic simulator of a debutanizer plant. Decision tree inducer can directly extract reasonable operational rules from the data-set with no previous knowledge. By integrating the feature-subset selection wrapper algorithm, Naive-Bayes classifier and nearest-neighbor classifier can also estimate the action of operation successfully.

Yoshiyuki Yamashita | Y. Yamashita

[1] J. Ross Quinlan,et al. Improved Use of Continuous Attributes in C4.5 , 1996, J. Artif. Intell. Res..

[2] C. McGreavy,et al. Data Mining and Knowledge Discovery for Process Monitoring and Control , 1999 .

[3] Kenneth A. Kaufman,et al. Data Mining and Knowledge Discovery: A Review of Issues and a Multistrategy Approach , 1997 .

[4] David W. Aha,et al. Tolerating Noisy, Irrelevant and Novel Attributes in Instance-Based Learning Algorithms , 1992, Int. J. Man Mach. Stud..

[5] Pat Langley,et al. Tractable Average-Case Analysis of Naive Bayesian Classifiers , 1999, ICML.

[6] J. Ross Quinlan,et al. C4.5: Programs for Machine Learning , 1992 .

[7] Tom M. Mitchell,et al. Machine Learning and Data Mining , 2012 .

[8] Padhraic Smyth,et al. From Data Mining to Knowledge Discovery: An Overview , 1996, Advances in Knowledge Discovery and Data Mining.