分子生物学（启动子基因序列）数据集，可用于评估一种混合学习算法（KBANN）

Data Set Information: This dataset has been developed to help evaluate a "hybrid" learning algorithm ("KBANN") that uses examples to inductively refine preexisting knowledge. Using a "leave-one-out" methodology, the following errors were produced by various ML algorithms. (See Towell, Shavlik, & Noordewier, 1990, for details.) System -- Errors -- Comments ---------------------------------------------------------------- KBANN -- 4/106 -- a hybrid ML system BP -- 8/106 -- std backprop with one hidden layer O'Neill -- 12/106 -- ad hoc technique from the bio. lit. Near-Neigh -- 13/106 -- a nearest-neighbor algo (k=3) ID3 -- 19/106 -- Quinlan's decision-tree builder Type of domain: non-numeric, nominal (one of A, G, T, C) Note: DNA nucleotides can be grouped into a hierarchy, as shown below: X (any) / (purine) R Y (pyrimidine) / / A G T C Here is that hierachy in a text-friendly format: X (any) . R (purine) . . A . . G . Y (pyrimidine) . . T . . C Attribute Information: 1. One of {+/-}, indicating the class ("+" = promoter). 2. The instance name (non-promoters named by position in the 1500-long nucleotide sequence provided by T. Record). 3-59. The remaining 57 fields are the sequence, starting at position -50 (p-50) and ending at position +7 (p7). Each of these fields is filled by one of {a, g, t, c}. Relevant Papers: Harley, C. and Reynolds, R. 1987. "Analysis of E. Coli Promoter Sequences." Nucleic Acids Research, 15:2343-2361. [Web link] Towell, G., Shavlik, J. and Noordewier, M. 1990. "Refinement of Approximate Domain Theories by Knowledge-based Artificial Neural Networks." In Proceedings of the Eighth National Conference on Artificial Intelligence (AAAI-90). [Web link] Papers That Cite This Data Set1: Ken Tang and Ponnuthurai N. Suganthan and Xi Yao and A. Kai Qin. Linear dimensionalityreduction using relevance weighted LDA. School of Electrical and Electronic Engineering Nanyang Technological University. 2005. [View Context]. Wei-Chun Kao and Kai-Min Chung and Lucas Assun and Chih-Jen Lin. Decomposition Methods for Linear Support Vector Machines. Neural Computation, 16. 2004. [View Context]. Aik Choon Tan and David Gilbert. An Empirical Comparison of Supervised Machine Learning Techniques in Bioinformatics. APBC. 2003. [View Context]. Giorgio Valentini. Ensemble methods based on bias--variance analysis Theses Series DISI-TH-2003. Dipartimento di Informatica e Scienze dell'Informazione . 2003. [View Context]. Zoubin Ghahramani and Hyun-Chul Kim. Bayesian Classifier Combination. Gatsby Computational Neuroscience Unit University College London. 2003. [View Context]. Jinyan Li and Limsoon Wong. Using Rules to Analyse Bio-medical data: A Comparison between C4.5 and PCL. WAIM. 2003. [View Context]. Michael G. Madden. evaluation of the Performance of the Markov Blanket Bayesian Classifier Algorithm. CoRR, csLG/0211003. 2002. [View Context]. Mukund Deshpande and George Karypis. evaluation of Techniques for Classifying Biological Sequences. PAKDD. 2002. [View Context]. Takashi Matsuda and Hiroshi Motoda and Tetsuya Yoshida and Takashi Washio. Mining Patterns from Structured Data by Beam-Wise Graph-based Induction. Discovery Science. 2002. [View Context]. Marina Meila and Michael I. Jordan. Learning with Mixtures of Trees. Journal of Machine Learning Research, 1. 2000. [View Context]. Jie Cheng and Russell Greiner. Comparing Bayesian Network Classifiers. UAI. 1999. [View Context]. Ismail Taha and Joydeep Ghosh. Symbolic Interpretation of Artificial Neural Networks. IEEE Trans. Knowl. Data Eng, 11. 1999. [View Context]. Cesar Guerra-Salcedo and L. Darrell Whitley. Genetic Approach to Feature Selection for Ensemble Creation. GECCO. 1999. [View Context]. Mark A. Hall and Lloyd A. Smith. Feature Selection for Machine Learning: Comparing a Correlation-based Filter Approach to the Wrapper. FLAIRS Conference. 1999. [View Context]. Mark A. Hall. Department of Computer Science Hamilton, NewZealand Correlation-based Feature Selection for Machine Learning. Doctor of Philosophy at The University of Waikato. 1999. [View Context]. Creators: 1. promoter instances: C. Harley (CHARLEY '@' McMaster.CA) and R. Reynolds 2. non-promoter instances and domain theory: M. Noordewier -- (non-promoters derived from work of lab of Prof. Tom Record, University of Wisconsin Biochemistry Department) Donor: M. Noordewier and J. Shavlik, {noordewi,shavlik}@cs.wisc.edu