Glass Identification Data Set
Download: Data Folder, Data Set Description
Abstract: From USA Forensic Science Service; 6 types of glass; defined in terms of their oxide content (i.e. Na, Fe, K, etc)
Data Set Characteristics:
Number of Instances:
Number of Attributes:
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Central Research Establishment
Home Office Forensic Science Service
Aldermaston, Reading, Berkshire RG7 4PN
Vina Spiehler, Ph.D., DABFT
Diagnostic Products Corporation
(213) 776-0180 (ext 3014)
Data Set Information:
Vina conducted a comparison test of her rule-based system, BEAGLE, the nearest-neighbor algorithm, and discriminant analysis. BEAGLE is a product available through VRS Consulting, Inc.; 4676 Admiralty Way, Suite 206; Marina Del Ray, CA 90292 (213) 827-7890 and FAX: -3189. In determining whether the glass was a type of "float" glass or not, the following results were obtained (# incorrect answers):
Type of Sample -- Beagle -- NN -- DA
Windows that were float processed (87) -- 10 -- 12 -- 21
Windows that were not: (76) -- 19 -- 16 -- 22
The study of classification of types of glass was motivated by criminological investigation. At the scene of the crime, the glass left can be used as evidence...if it is correctly identified!
1. Id number: 1 to 214
2. RI: refractive index
3. Na: Sodium (unit measurement: weight percent in corresponding oxide, as are attributes 4-10)
4. Mg: Magnesium
5. Al: Aluminum
6. Si: Silicon
7. K: Potassium
8. Ca: Calcium
9. Ba: Barium
10. Fe: Iron
11. Type of glass: (class attribute)
-- 1 building_windows_float_processed
-- 2 building_windows_non_float_processed
-- 3 vehicle_windows_float_processed
-- 4 vehicle_windows_non_float_processed (none in this database)
-- 5 containers
-- 6 tableware
-- 7 headlamps
Ian W. Evett and Ernest J. Spiehler. Rule Induction in Forensic Science. Central Research Establishment. Home Office Forensic Science Service. Aldermaston, Reading, Berkshire RG7 4PN
Papers That Cite This Data Set1:
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Krzysztof Krawiec. Genetic Programming-based Construction of Features for Machine Learning and Knowledge Discovery Tasks. Institute of Computing Science, Poznan University of Technology. 2002. [View Context].
Michail Vlachos and Carlotta Domeniconi and Dimitrios Gunopulos and George Kollios and Nick Koudas. Non-linear dimensionality reduction techniques for classification and visualization. KDD. 2002. [View Context].
Giorgio Valentini and Francesco Masulli. NEURObjects: an object-oriented library for neural network development. Neurocomputing, 48. 2002. [View Context].
D. I. S I and Francesco Masulli and Giorgio Valentini and D. I. S. Universit#a di Genova. Dipartimento di Informatica e Scienze dell' Informazione. 2001. [View Context].
Mark A. Hall. Correlation-based Feature Selection for Discrete and Numeric Class Machine Learning. ICML. 2000. [View Context].
Petri Kontkanen and Petri Myllym and Tomi Silander and Henry Tirri and Peter Gr. On predictive distributions and Bayesian networks. Department of Computer Science, Stanford University. 2000. [View Context].
Thierry Denoeux. A neural network classifier based on Dempster-Shafer theory. IEEE Transactions on Systems, Man, and Cybernetics, Part A, 30. 2000. [View Context].
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. Prototype Selection for Composite Nearest Neighbor Classifiers. Department of Computer Science University of Massachusetts. 1997. [View Context].
Georg Thimm and E. Fiesler. Optimal Setting of Weights, Learning Rate, and Gain. E S E A R C H R E P R O R T I D I A P. 1997. [View Context].
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Ethem Alpaydin. Voting over Multiple Condensed Nearest Neighbors. Artif. Intell. Rev, 11. 1997. [View Context].
Jan C. Bioch and D. Meer and Rob Potharst. Bivariate Decision Trees. PKDD. 1997. [View Context].
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Ron Kohavi and Mehran Sahami. Error-Based and Entropy-Based Discretization of Continuous Features. KDD. 1996. [View Context].
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Francesco Masulli and Giorgio Valentini. Quantitative Evaluation of Dependence among Outputs in ECOC Classifiers Using Mutual Information Based Measures. Universitdi Genova DISI - Dipartimento di Informatica e Scienze dell'Informazione INFM - Istituto Nazionale per la Fisica della Materia. [View Context].
Rong-En Fan and P. -H Chen and C. -J Lin. Working Set Selection Using the Second Order Information for Training SVM. Department of Computer Science and Information Engineering National Taiwan University. [View Context].
Yin Zhang and W. Nick Street. Bagging with Adaptive Costs. Management Sciences Department University of Iowa Iowa City. [View Context].
Ping Zhong and Masao Fukushima. Second Order Cone Programming Formulations for Robust Multi-class Classification. [View Context].
Karthik Ramakrishnan. UNIVERSITY OF MINNESOTA. [View Context].
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Federico Divina and Elena Marchiori. Handling Continuous Attributes in an Evolutionary Inductive Learner. Department of Computer Science Vrije Universiteit. [View Context].
James J. Liu and James Tin and Yau Kwok. An Extended Genetic Rule Induction Algorithm. Department of Computer Science Wuhan University. [View Context].
Francesco Masulli and Giorgio Valentini. Comparing Decomposition Methods for Classification. Istituto Nazionale per la Fisica della Materia DISI - Dipartimento di Informatica e Scienze dell'Informazione. [View Context].
Alexander K. Seewald. Dissertation Towards Understanding Stacking Studies of a General Ensemble Learning Scheme ausgefuhrt zum Zwecke der Erlangung des akademischen Grades eines Doktors der technischen Naturwissenschaften. [View Context].
H. Altay G uvenir and Aynur Akkus. WEIGHTED K NEAREST NEIGHBOR CLASSIFICATION ON FEATURE PROJECTIONS. Department of Computer Engineering and Information Science Bilkent University. [View Context].
Ron Kohavi and Brian Frasca. Useful Feature Subsets and Rough Set Reducts. the Third International Workshop on Rough Sets and Soft Computing. [View Context].
H. Altay Guvenir. A Classification Learning Algorithm Robust to Irrelevant Features. Bilkent University, Department of Computer Engineering and Information Science. [View Context].
Suresh K. Choubey and Jitender S. Deogun and Vijay V. Raghavan and Hayri Sever. A comparison of feature selection algorithms in the context of rough classifiers. [View Context].
Stefan Aeberhard and Danny Coomans and De Vel. THE PERFORMANCE OF STATISTICAL PATTERN RECOGNITION METHODS IN HIGH DIMENSIONAL SETTINGS. James Cook University. [View Context].
Chih-Wei Hsu and Cheng-Ru Lin. A Comparison of Methods for Multi-class Support Vector Machines. Department of Computer Science and Information Engineering National Taiwan University. [View Context].
C. Titus Brown and Harry W. Bullen and Sean P. Kelly and Robert K. Xiao and Steven G. Satterfield and John G. Hagedorn and Judith E. Devaney. Visualization and Data Mining in an 3D Immersive Environment: Summer Project 2003. [View Context].
. Eectiveness of Error Correcting Output Coding methods in ensemble and monolithic learning machines. Dipartimento di Informatica, Universitdi Pisa. [View Context].
Zhi-Hua Zhou and Xu-Ying Liu. Training Cost-Sensitive Neural Networks with Methods Addressing the Class Imbalance Problem. [View Context].
Aynur Akku and H. Altay Guvenir. Weighting Features in k Nearest Neighbor Classification on Feature Projections. Department of Computer Engineering and Information Science Bilkent University. [View Context].
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