License. The Wisconsin Breast Cancer Database (WBCD) dataset has been widely used in research experiments. The Breast Cancer Dataset is a dataset of features computed from breast mass of candidate patients. [View Context].Geoffrey I. Webb. [View Context].Rudy Setiono and Huan Liu. The malignant class of this dataset is downsampled to 21 points, which are considered as outliers, while points in the benign class are considered inliers. UCI Machine Learning • updated 4 years ago (Version 2) Data Tasks (2) Notebooks (1,498) Discussion (34) Activity Metadata. Most of publications focused on traditional machine learning methods such as decision trees and decision tree-based ensemble methods . Breast Cancer Wisconsin (Diagnostic) Data Set Predict whether the cancer is benign or malignant. Wisconsin Breast Cancer Dataset. Wolberg and O.L. Approximate Distance Classification. Download data. Thanks go to M. Zwitter and M. Soklic for providing the data. [View Context].Lorne Mason and Peter L. Bartlett and Jonathan Baxter. [View Context].Baback Moghaddam and Gregory Shakhnarovich. STAR - Sparsity through Automated Rejection. Breast Cancer Wisconsin (Diagnostic) Data Set Predict whether the cancer is benign or malignant. Dept. Mangasarian: "Multisurface method of pattern separation for medical diagnosis applied to breast cytology", Proceedings of the National Academy of Sciences, U.S.A., Volume 87, December 1990, pp 9193-9196. Sys. 1, pp. An Empirical Assessment of Kernel Type Performance for Least Squares Support Vector Machine Classifiers. Direct Optimization of Margins Improves Generalization in Combined Classifiers. Normal Nucleoli: 1 - 10 10. F. Keller, E. Muller, K. Bohm.“HiCS: High-contrast subspaces for density-based outlier ranking.” ICDE, 2012. This breast cancer domain was obtained from the University Medical Centre, Institute of Oncology, Ljubljana, Yugoslavia. There are two classes, benign and malignant. Rui Sarmento; Original Wisconsin Breast Cancer Database Analysis performed with Statsframe ULTRA. of Decision Sciences and Eng. 2002. [View Context].Lorne Mason and Peter L. Bartlett and Jonathan Baxter. [View Context].Wl odzisl and Rafal Adamczak and Krzysztof Grabczewski and Grzegorz Zal. as integer from 1 - 10. uniformity_cellsize. 850f1a5d Rahim Rasool authored Mar 19, 2020. 1997. O. L. Mangasarian and W. H. Wolberg: "Cancer diagnosis via linear programming", SIAM News, Volume 23, Number 5, September 1990, pp 1 & 18. These are consecutive patients seen by Dr. Wolberg since 1984, and include only those cases exhibiting invasive breast cancer and no evidence of distant metastases at the time of diagnosis. [View Context].Chotirat Ann and Dimitrios Gunopulos. William H. Wolberg and O.L. Usability. Hybrid Extreme Point Tabu Search. [View Context].Yuh-Jeng Lee. It is a dataset of Breast Cancer patients with Malignant and Benign tumor. Uniformity of Cell Size: 1 - 10 4. The University of Birmingham. [View Context].Nikunj C. Oza and Stuart J. Russell. [View Context].Wl odzisl/aw Duch and Rudy Setiono and Jacek M. Zurada. [View Context].Bart Baesens and Stijn Viaene and Tony Van Gestel and J. [View Context].Adam H. Cannon and Lenore J. Cowen and Carey E. Priebe. 2002. breastcancer: Breast Cancer Wisconsin Original Data Set in OneR: One Rule Machine Learning Classification Algorithm with Enhancements rdrr.io Find an R package R language docs Run R in your browser business_center. Analysis and Predictive Modeling with Python. K. P. Bennett & O. L. Mangasarian: "Robust linear programming discrimination of two linearly inseparable sets", Optimization Methods and Software 1, 1992, 23-34 (Gordon & Breach Science Publishers). (1992). For the project, I used a breast cancer dataset from Wisconsin University. 1. J. Artif. Dataset containing the original Wisconsin breast cancer data. OPUS: An Efficient Admissible Algorithm for Unordered Search. Each record represents follow-up data for one breast cancer case. 17, no. Subsampling for efficient and effective unsupervised outlier detection ensembles. [View Context].Adil M. Bagirov and Alex Rubinov and A. N. Soukhojak and John Yearwood. Constrained K-Means Clustering. Department of Mathematical Sciences The Johns Hopkins University. Breast Cancer Wisconsin (Diagnostic) Dataset. 2000. NIPS. 2004. bcancer.Rd. Department of Mathematical Sciences Rensselaer Polytechnic Institute. Data Set Information: Features are computed from a digitized image of a fine needle aspirate (FNA) of a breast mass. 1998. Statistical methods for construction of neural networks. 1998. Knowl. As we can see in the NAMES file we have the following columns in the dataset: A Parametric Optimization Method for Machine Learning. This data set is in the collection of Machine Learning Data Download breast-cancer-wisconsin-wdbc breast-cancer-wisconsin-wdbc is 122KB compressed! Machine Learning, 38. 1998. Smooth Support Vector Machines. 1995. 1996. [View Context].Endre Boros and Peter Hammer and Toshihide Ibaraki and Alexander Kogan and Eddy Mayoraz and Ilya B. Muchnik. School of Information Technology and Mathematical Sciences, The University of Ballarat. 15. perc_overlap . Discriminative clustering in Fisher metrics. [View Context]. 700 lines (700 sloc) 19.6 KB Raw Blame. [Web Link]. Diversity in Neural Network Ensembles. "-//W3C//DTD HTML 4.01 Transitional//EN\">, Breast Cancer Wisconsin (Original) Data Set Data. This grouping information appears immediately below, having been removed from the data itself: Group 1: 367 instances (January 1989) Group 2: 70 instances (October 1989) Group 3: 31 instances (February 1990) Group 4: 17 instances (April 1990) Group 5: 48 instances (August 1990) Group 6: 49 instances (Updated January 1991) Group 7: 31 instances (June 1991) Group 8: 86 instances (November 1991) ----------------------------------------- Total: 699 points (as of the donated datbase on 15 July 1992) Note that the results summarized above in Past Usage refer to a dataset of size 369, while Group 1 has only 367 instances. Sample code number: id number 2. All Rights Reserved. The malignant class of this dataset is downsampled to 21 points, which are considered as outliers, while points in the benign class are considered inliers. uni. A brief description of the dataset and some tips will also be discussed. [View Context].Krzysztof Grabczewski and Wl/odzisl/aw Duch. Department of Information Systems and Computer Science National University of Singapore. School of Computing National University of Singapore. Data-dependent margin-based generalization bounds for classification. Dataset Collection. Data used for the project. Blue and Kristin P. Bennett. [View Context].Hussein A. Abbass. 2002. Street, W.H. breast cancerデータはUCIの機械学習リポジトリ―にあるBreast Cancer Wisconsin (Diagnostic) Data Setのコピーで、乳腺腫瘤の穿刺吸引細胞診(fine needle aspirate (FNA) of a breast mass)のデジタル画像から計算されたデータ。 An Ant Colony Based System for Data Mining: Applications to Medical Data. Theoretical foundations and algorithms for outlier ensembles. K-nearest neighbour algorithm is used to predict whether is patient is having cancer … I opened it with Libre Office Calc add the column names as described on the breast-cancer-wisconsin NAMES file, and save the file as csv. 1996. Experimental comparisons of online and batch versions of bagging and boosting. Microsoft Research Dept. National Science Foundation. Department of Information Systems and Computer Science National University of Singapore. An Implementation of Logical Analysis of Data. The machine learning methodology has long been used in medical diagnosis . 1996. The database therefore reflects this chronological grouping of the data. [1] Papers were automatically harvested and associated with this data set, in collaboration 2000. CEFET-PR, CPGEI Av. [View Context].András Antos and Balázs Kégl and Tamás Linder and Gábor Lugosi. Nearest Neighbor is defined by the characteristics of classifying unlabeled examples by assigning then the class of similar labeled examples (tomato – is it a fruit or vegetable? pl. Computational intelligence methods for rule-based data understanding. [View Context].Charles Campbell and Nello Cristianini. This is a dataset about breast cancer occurrences. A hybrid method for extraction of logical rules from data. 2002. 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Holden S. Parpinelli and Heitor Lopes. This data Set Information: features are computed from breast mass of candidate patients method... Rudy Setiono and Huan Liu of supervised Machine Learning methodology has long been used in experiments! Katholieke Universiteit Leuven Campbell and Nello Cristianini collection procedure, the University of Wisconsin Hospitals, Madison from Dr. H.. ( 2 for benign, 4 for malignant ), Wolberg, W.H., & Mangasarian, O.L outlier ”... Bart De Moor and Jan Vanthienen and Katholieke Universiteit Leuven on the following 11 variables department of.: Ant Colony Algorithm for classification Rule Discovery University of Wisconsin, 1210 Dayton! Feature value… Download data are computed from breast mass ) data Set whether... Or benign tumour and Samuel Kaski and Janne Sinkkonen Combined Classifiers Rule Discovery the measurements for breast cancer dataset be... Of bagging and boosting uniformity of Cell Shape: 1 = Multi-dimensional point,! 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