Method, system, and apparatus for casual discovery and...

Data processing: artificial intelligence – Machine learning

Reexamination Certificate

Rate now

  [ 0.00 ] – not rated yet Voters 0   Comments 0

Details

C706S014000, C706S046000

Reexamination Certificate

active

07117185

ABSTRACT:
A method of determining a local causal neighborhood of a target variable from a data set can include identifying variables of the data set as candidates of the local causal neighborhood using statistical characteristics, and including the identified variables within a candidate set. False positive variables can be removed from the candidate set according to further statistical characteristics applied to each variable of the candidate set. The remaining variables of the candidate set can be identified as the local causal neighborhood of the target variable.

REFERENCES:
patent: 5704017 (1997-12-01), Heckerman et al.
patent: 5805776 (1998-09-01), Juengst et al.
patent: 6076083 (2000-06-01), Baker
patent: 6246975 (2001-06-01), Rivonelli et al.
patent: 6336108 (2002-01-01), Thiesson et al.
patent: 6456622 (2002-09-01), Skaanning et al.
patent: 6480832 (2002-11-01), Nakisa
Clark Glymour, Computation, Causation, and Discovery, Jun. 1999, The MIT Press, Chapter ONE.
C. F. Aliferis et al, HITON, A Novel Markov Blanket Algorithm for Optimal Variable Selection, 2003, AMIA, (five).
Ioannis Tsamardinos et al, Time and Sample Efficient Discovery of Markov Blankets and Direct Causal Relations, ACM, (ten).
Burges, C.J.C., “A tutorial on Support Vector Machines for Pattern Recognition”, Data Mining and Knowledge Discovery, vol. 2, No. 2, pp. 1-47, 1998.
Caruana, R., et al., “Greedy Attribute Selection”, Int'l. Conf. on Machine Learning, 1994.
Cheng, J., et al., “Comparing Bayesian Network Classifiers”, 15th Conf. on Uncertainty in Artificial Intelligence, UAI, 1999.
Cheng, J., et al., “KDD Cup 2001 Report”, SIGKDD Explorations, vol. 3, Issue 2, pp. 47-64, 2002.
Cooper, G.F., et al., “A Bayesian Method for the Induction of Probabilistic Networks from Data”, Machine Learning 9, pp. 309-347, 1992.
Guyon, I., et al., “Gene Selection for Cancer Classification Using Support Vector Machines”, Machine Learning, vol. 46, pp. 389-422, (2002).
Heckerman, D., “A Bayesian Approach to Learning Causal Networks”, Microsoft Research Tech. Rpt. MSR-TR-95-04, Mar. 1995.
Kohavi, R., et al., “Wrappers for Feature Subset Selection”, Artificial Intelligence, vol. 97, No. 1-2, pp. 273-324, May 20, 1997.
Koller, D., et al., “Toward Optimal Feature Selection”, 13th Int'l. Conf. in Machine Learning, 1996.
Platt, J.C., “Sequential Minimal Optimization: A Fast Algorithm for Training Support Vector Machines”, Microsoft Research Tech. Rpt. MSR-TR-98-14, Apr. 21, 1998.
Provost, F., et al., “The Case Against Accuracy Estimation for Comparing Induction Algorithms”, 15th Int'l. Conf. on Machine Learning, 1998.
Tsarmardinos, I., et al., “Algorithms for Local Causal Discovery”, Vanderbilt University Tech. Rpt. DSL-02-03, Jul. 1, 2002.
Weston, J., et al., “Feature Selection and Transduction for Prediction of Molecular Bioactivity for Drug Design”, Bioinformatics, vol. 1, No. 2002, pp. 1-8, 2002.
Chang, C.C., et al., “LIBSVM: A Library for Support Vector Machines (Version 2.31)”, Dept. of Comp. Science and Info. Engineering, Nat'l. Taiwan Univ., Sep. 7, 2001.
Cheng, J., et al., “Learning Bayesian Networks from Data: An Efficient Approach Based on Information Theory”, University of Alberta Tech. Rpt., 1998.
Chickering, D.M., et al., “Learning Bayesian Networks is NP-Hard”, Microsoft Research Tech. Rpt., MSR-TR-94-17, 1994.
Mani, S., et al., “A Simulation Study of Three Related Causal Data Mining Algorithms”, Artificial Intelligence and Statistics, pp. 73-80, 2001.
Arnone, M. I., et al., “The Hardwiring of Development: Organization and Function of Genomic Regulatory Systems”, Development, Vo. 12, No. 4, pp. 1851-1864, 1997.
Blum, A.L., et al., “Selection of Relevant Features and Examples in Machine Learning”, Artificial Intelligence, vol. 92, No. 1-2, pp. 245-271, 1997.
Cheng, J., et al., “Learning Bayesian Networks from Data: An Information -Theory Based Approach”, Proc. of 6th ACM Int'l. Conf. on Information and Knowledge Mgmt., 1997.
Provan, G.M., et al., “Learning Bayesian Networks Using Feature Selection”, 5th Int'l. Workshop on Artificial Intelligence and Statistics, 1995.
Scott, M.J.J., et al., “Parcel: Feature Subset Selection in Variable Cost Domains”, Cambridge University, May 1998.
Aliferis, C.F., et al., “Markov Blanket Induction for Feature Selection”, Vanderbilt University, Discovery Systems Laboratory Tech. Rpt. DLS-02-02, 2002.
Weston, J., et al., “Feature Selection for SVMs”, NIPS, pp. 668-674, 2000.
Wolpert, D.H., et al., “No Free Lunch Theorems for Optimization”, IEEE Transactions on Evolutionary Computation, vol. 1, No. 1, pp. 67-82, Apr. 1997.
Friedman, N., et al., “Data Analysis With Bayesian Networks: A Bootstrap Approach”, 15th Conf. on Uncertainty in Artificial Intelligence, UAI-99, 1999.
Friedman, N., et al., “Learning Bayesian Network Structure from Massive Datasets: The ‘Sparse Candidate’ Algorithm”, 15th Conf. on Uncertainty in Art. Intelligence, 1999.
Heckerman, D., et al., “A Tutorial on Learning With Bayesian Networks”, Microsoft Research Tech. Rpt. MSR-TR-95-06, 1995.
Almuallim, H., et al., “Efficient Algorithms for Identifying Relevant Features”, 9th Canadian Conf. on Artificial Intelligence, 1992.
Kononenko, I., “Estimating Attributes: Analysis and Extensions of RELIEF”, European Conf. on Machine Learning, 1994.
Aliferis, C.F., et al., “Large-Scale Feature Selection Using Markov Blanket Induction for the Prediction of Protein-Drug Binding”, Vanderbilt U. Tech. Rpt. DSL TR-02-06, 2002.
Tsamardinos, I., et al., “Algorithms for Large Scale Local Causal Discovery”, Vanderbilt University.
Aliferis, C.F., et al., “Methods for Principled Feature Selection for Classification, Causal Discovery, and Causal Manipulation”, Vanderbilt U. Tech. Rpt. DSL-02-01, Mar. 2002.
Tsamardinos, I., et al., “Towards Principled Feature Selection: Relevancy, Filters and Wrappers”, AI in Statistics, 2003.
Tsamardinos, I., et al., “Time and Sample Efficient Discovery of Markov Blankets and Direct Causal Relations”, KDD 2003, 2002.
Aliferis, C.F., et al., “HITON, A Novel Markov Blanket Algorithm for Optimal Variable Selection”, Proc. of 2003 Amer. Med. Informatics Assoc.(AMIA) Annual Symposium, 2003.
Tsamardinos, I., et al., “Scaling-Up Bayesian Network Learning to Thousands of Variables Using Local Learning Techniques”, Vanderbilt Univ. Tech. Rpt. DSL TR-03-02, Mar. 2003.
Hutter, M., “Distribution of Mutual Information”, Technical Report IDSIA-13-01, Dec. 15, 2001.
Margaritis, D., et al., “Bayesian Network Induction Via Local Neighborhoods”, Carnegie Mellon Univ. Tech. Rpt. CMU-CS-99-134, Aug. 1999.
Duin, R.P.W., “Classifiers in Almost Empty Spaces”, Proc. of 15th Int'. Conf. on Pattern Recognition, Sep. 3-8, 2000.
Aliferis, et al., “An Eval. of an Algorithm for Inductive Learning of Bayesian Belief Nets. Using Simulated Data Sets”, Uncertainty in Art. Intel., 10th Conf. Proc., 1994.
Meek, C., “Strong Completeness & Faithfulness in Bayesian Networks,” Uncertainty in Artifical Intelligence, 11th Conf. Proc., 1995.
Pearl, J., “Learning Structure From Data”, Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference, Chp. 8, pp. 381-414, (Sep. 1988).
Kohavi, R., et al., “The Wrapper Approach”, Feature Extraction, Construction & Selection: A Data Mining Perspective, Chp. 3, pp. 33-50, (Jul. 1998).
Wang, H., et al., “Relevance Approach to Feature Subset Selection”, Feature Extraction, Construction & Selection: A Data Mining Perspective, Chp. 6, pp. 85-99, (Jul. 1998

LandOfFree

Say what you really think

Search LandOfFree.com for the USA inventors and patents. Rate them and share your experience with other people.

Rating

Method, system, and apparatus for casual discovery and... does not yet have a rating. At this time, there are no reviews or comments for this patent.

If you have personal experience with Method, system, and apparatus for casual discovery and..., we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Method, system, and apparatus for casual discovery and... will most certainly appreciate the feedback.

Rate now

     

Profile ID: LFUS-PAI-O-3649904

  Search
All data on this website is collected from public sources. Our data reflects the most accurate information available at the time of publication.