Evolutionary computing for knowledge discovery in medical diagnosis

Banzhaf W, Nordin P, Keller RE, Francone FD. Genetic programming: an introduction on the automatic evolution of computer programs and its applications. San Francisco (CA): Kaufmann (Morgan); 1998. Bojarczuk, 2000, Genetic programming for knowledge discovery in chest-pain diagnosis, IEEE Eng. Med. Biol. Mag., 4, 38, 10.1109/51.853480 Brameier, 2001, A comparison of linear genetic programming neural networks in medical data mining, IEEE Trans. Evol. Comput., 5, 17, 10.1109/4235.910462 Cattral, 1999, Rule acquisition with a genetic algorithm, Proc. IEEE Cong. Evol. Comput., 1, 125 Cestnik G, Konenenko I, Bratko I. Assistant-86: a knowledge-elicitation tool for sophisticated users. In: Bratko I, Lavrac N, editors. Machine learning. Wilmslow: Sigma Press; 1987. p. 31–45. Chang YH, Zheng B, Wang XH, Good WF. Computer-aided diagnosis of breast cancer using artificial neural networks: comparison of backpropagation and genetic algorithms. In: Proceedings of the International Joint Conference on Neural Networks, Washington, DC, USA, vol. 5. Washington (DC): IEEE Press; 1999. p. 3674–9. Congdon CB. Classification of epidemiological data: a comparison of genetic algorithm and decision tree approaches. In: Proceedings of the IEEE Congress on Evolutionary Computation, La Jolla Marriott, San Diego, CA, USA, vol. 1. Piscataway (NJ): IEEE Press; 2000. p. 442–9. Fidelis MV, Lopes HS, Freitas AA. Discovering comprehensible classification rules with a genetic algorithm. In: Proceedings of the IEEE Congress on Evolutionary Computation, La Jolla Marriott, San Diego, CA, USA, vol. 1. Piscataway (NJ): IEEE Press; 2000. p. 805–10. Frank E, Witten IH. Generating accurate rule sets without global optimization. In: Proceedings of the 15th International Conference Machine Learning (ICML’98), Madison, WI, USA. San Francisco (CA): Kaufmann (Morgan); 1998. p. 144–51. Freitas AA. A survey of evolutionary algorithms for data mining and knowledge discovery. In: Ghosh A, Tsutsui S, editors. Advances in evolutionary computation. Berlin: Springer; 2002. Garner SR. WEKA: the Waikato environment for knowledge analysis. In: Proceedings of the New Zealand Computer Science Research Students Conference, The University of Waikato, Hamilton, New Zealand, 1995. p. 57–64. Goldberg DE. Genetic algorithms in search, optimization and machine learning. Reading (MA): Addison-Wesley; 1989. Howard LM, D’Angelo DJ. The GA-P: a genetic algorithm and genetic programming hybrid. IEEE Expert 1995;(June):11–5. Hruschka, 2000, A clustering genetic algorithm for extracting rules from supervised neural network models in data mining tasks, Int. J. Comput. Syst. Signals, 1, 17 Hu YJ. Constructive induction covering attributes spectrum. In: Liu H, Motoda H, editors. Feature extraction construction and selection: a data mining perspective. Norwell (MA): Kluwer Academic Publishers; 1998. p. 257–69. John GH, Langley P. Estimating continuous distributions in Bayesian classifiers. In: Proceedings of the 11th Conference on Uncertainty in Artificial Intelligence, McGill University, Montreal, Que., Canada. San Mateo (CA): Kaufmann (Morgan); 1995. p. 338–45. Joshi MV, Agarwal RC, Kumar V. Mining needles in a haystack: classifying rare classes via two-phase rule induction. In: Proceedings of the 2001 ACM SIGMOD International Conference on Management of Data, Santa Barbara, CA; 2001. p. 91–101. Kim, 2000, Genetic algorithms approach to feature discretization in artificial neural networks for the prediction of stock price index, Expert Syst. Appl., 19, 125, 10.1016/S0957-4174(00)00027-0 Komosinski, 2000, Evolutionary weighting of image features for diagnosing of CNS tumors, Artif. Intell. Med., 19, 25, 10.1016/S0933-3657(99)00048-2 Koza JR. Genetic programming: on the programming of computers by means of natural selection. Cambridge (MA): MIT Press; 1992. Kupinski, 1999, Multiobjective genetic optimization of diagnostic classifiers with implications for generating receiver operating characteristic curves, IEEE Trans. Med. Imaging, 18, 675, 10.1109/42.796281 Liu, 2001, An improved Naı̈ve Bayesian classifier technique coupled with a novel input solution method, IEEE Trans. Syst. Man Cybernetics C: Appl. Rev., 31, 249, 10.1109/5326.941848 Meesad P, Yen GG. A hybrid intelligent system for medical diagnosis. In: Proceedings of International Joint Conference on Neural Networks, vol. 4. Piscataway (NJ): IEEE Press; 2001. p. 2558–63. Michalewicz Z. Genetic algorithms + data structures = evolution programs. London: Kluwer Academic Publishers; 1994. Montgomery DC, Runger GC, Hubele NF. Engineering statistics. 2nd ed. New York: Wiley; 2001. Peña-Reyes, 1999, A fuzzy-genetic approach to breast cancer diagnosis, Artif. Intell. Med., 17, 131, 10.1016/S0933-3657(99)00019-6 Peña-Reyes, 2000, Evolutionary computation in medicine: an overview, Artif. Intell. Med., 19, 1, 10.1016/S0933-3657(99)00047-0 Peña-Reyes, 2001, Fuzzy CoCo: a cooperative coevolutionary approach to fuzzy modeling, IEEE Trans. Fuzzy Syst., 9, 727, 10.1109/91.963759 Prechelt, 1995, Some notes on neural learning algorithm benchmarking, NeuralComputing, 9, 343, 10.1016/0925-2312(95)00084-1 Quinlan JR. C4.5: programs for machine learning. San Mateo (CA): Kaufmann (Morgan); 1992. Setiono, 1996, Extracting rules from pruned neural networks for breast cancer diagnosis, Artif. Intell. Med., 8, 37, 10.1016/0933-3657(95)00019-4 Setiono, 2000, Generating concise and accurate classification rules for breast cancer diagnosis, Artif. Intell. Med., 18, 205, 10.1016/S0933-3657(99)00041-X Street WN, Wolberg WH, Mangasarian OL. Nuclear feature extraction for breast tumor diagnosis. In: IS&T/SPIE 1993 International Symposium on Electronic Imaging: Science and Technology, vol. 1905, San Jose, CA, 1993. p. 861–70. Taha I, Gosh J. Symbolic interpretation of artificial neural networks. Technical report TR-97-01-106. Austin: The Computer and Vision Research Center, University of Texas; 1996. Tan, 2001, A multi-objective evolutionary algorithm toolbox for computer-aided multi-objective optimization, IEEE Trans. Syst. Man Cybernetics B (Cybernetics), 31, 537, 10.1109/3477.938259 Tan KC, Tay A, Lee TH, Heng CM. Mining multiple comprehensible classification rules using genetic programming. In: IEEE Congress on Evolutionary Computation, Honolulu, HI, 2002. p. 1302–7. Tan, 2002, Automating the drug scheduling of cancer chemotherapy via evolutionary computation, Artif. Intell. Med., 25, 169, 10.1016/S0933-3657(02)00014-3 Wang, 1998, Automatically integrating multiple rule sets in a distributed-knowledge environment, IEEE Trans. Syst. Man Cybernetics C: Appl. Rev., 28, 471, 10.1109/5326.704591 Wang, 2000, Integrating membership functions and fuzzy rule sets from multiple knowledge sources, Fuzzy Sets Syst., 112, 141, 10.1016/S0165-0114(97)00385-0 Witten IH, Frank E. Data mining: practical machine learning tools and techniques with Java implementations. San Francisco (CA): Kaufmann (Morgan); 1999. Wong ML, Leung KS. Data mining using grammar based genetic programming and applications. London: Kluwer Academic Publishers; 2000. Wong, 2000, Discovering knowledge from medical databases using evolutionary algorithms, IEEE Eng. Med. Biol. Mag., 19, 45, 10.1109/51.853481 Yao, 1997, A new evolutionary system for evolving artificial neural networks, IEEE Trans. Neural Netw., 8, 694, 10.1109/72.572107