Statlog项目数据集

Origin: The Stalog databases are a subset of the datasets used in the European Statlog project. Donor: Ross D. King Department of Statistics and Modelling Science University of Strathclyde Glasgow G1 1XH Scotland U.K. [phone] x 3033 Fax [phone] ross '@' turing.uk.ac Data Set Information: The databases available here were in used in the European StatLog project, which involves comparing the performances of machine learning, statistical, and neural network algorithms on data sets from real-world industrial areas including medicine, finance, image analysis, and engineering design. Not all of the databases used in the project are available in this repository. Databases: (a) Vehicle Silhouettes: The original purpose was to find a method of distinguishing 3D objects within a 2D image by application of an ensemble of shape feature extractors to the 2D silhouettes of the objects. (b) Landsat Satellite: The database consists of the multi-spectral values of pixels in 3x3 neighbourhoods in a satellite image, and the classification associated with the central pixel in each neighbourhood. The aim is to predict this classification, given the multi-spectral values. In the sample database, the class of a pixel is coded as a number. (c) Shuttle: The shuttle dataset contains 9 attributes all of which are numerical. Approximately 80% of the data belongs to class 1. (d) Australian Credit Approval: This file concerns credit card applications. All attribute names and values have been changed to meaningless symbols to protect confidentiality of the data. This database exists elsewhere in the repository (Credit Screening Database) in a slightly different form. (e) Heart Disease: This dataset is a heart disease database similar to a database already present in the repository (Heart Disease databases) but in a slightly different form. This database contains 13 attributes (which have been extracted from a larger set of 75). (f) Image Segmentation: This dataset is an image segmentation database similar to a database already present in the repository (Image segmentation database) but in a slightly different form. The instances were drawn randomly from a database of 7 outdoor images. The images were handsegmented to create a classification for every pixel. Each instance is a 3x3 region. (g) German Credit: This dataset classifies people described by a set of attributes as good or bad credit risks. Comes in two formats (one all numeric). Also comes with a cost matrix. Attribute Information: N/A Relevant Papers: Feng,C., Sutherland,A., King,S., Muggleton,S. & Henery,R. (1993). Comparison of Machine Learning Classifiers to Statistics and Neural Networks. AI & Stats Conf. 93. [Web Link]

起源：Stalog数据库（Stalog Databases）是欧洲Statlog项目（Statlog Project）所使用数据集的子集。捐赠方：罗斯·D·金（Ross D. King），英国苏格兰格拉斯哥斯特拉斯克莱德大学（University of Strathclyde）统计与建模科学系，通信地址：格拉斯哥G1 1XH，电话：[phone] 分机3033，传真：[phone]，电子邮箱：ross '@' turing.uk.ac。
数据集说明：本仓库提供的数据库曾应用于欧洲Statlog项目（Statlog Project），该项目旨在对比机器学习、统计方法以及神经网络算法在真实工业领域数据集上的表现，覆盖医学、金融、图像分析与工程设计等多个应用方向。本仓库并未收录该项目使用的全部数据库。
数据集列表：
(a) 车辆剪影（Vehicle Silhouettes）数据集：最初的研究目标为通过对物体的二维剪影应用集成形状特征提取器，探寻区分二维图像内三维物体的有效方法。
(b) 陆地卫星（Landsat Satellite）数据集：该数据库包含卫星图像中3×3邻域内像素的多光谱值，以及每个邻域中心像素对应的分类标签。任务目标为根据多光谱值预测该中心像素的分类。在示例数据库中，像素类别以数字编码形式呈现。
(c) 航天飞机（Shuttle）数据集：该数据集包含9个全为数值型的属性。约80%的数据属于类别1。
(d) 澳大利亚信用卡审批（Australian Credit Approval）数据集：该文件涉及信用卡申请审批任务。为保护数据隐私，所有属性名称与属性值均已替换为无意义的符号。该数据库在本仓库的其他位置以略有差异的形式存在，对应条目为信用筛查数据库（Credit Screening Database）。
(e) 心脏病（Heart Disease）数据集：本数据集是与本仓库中已有的心脏病数据库（Heart Disease databases）类似但格式略有差异的心脏病数据库。该数据集包含13个属性（从75个原始属性中提取得到）。
(f) 图像分割（Image Segmentation）数据集：本数据集是与本仓库中已有的图像分割数据库（Image segmentation database）类似但格式略有差异的图像分割数据库。样本实例从7幅户外图像的数据库中随机抽取，这些图像已被手动分割并为每个像素标注了分类标签。每个实例对应一个3×3的区域。
(g) 德国信贷（German Credit）数据集：该数据集用于根据一组属性描述将人群划分为良好信贷风险与不良信贷风险两类。提供两种格式的数据（其中一种全为数值型），同时附带代价矩阵。
属性信息：无
相关文献：Feng,C.、Sutherland,A.、King,S.、Muggleton,S. 与 Henery,R.（1993）. 机器学习分类器与统计方法、神经网络的对比研究. 1993年人工智能与统计会议（AI & Stats Conf. 93）。[网页链接]

搜集汇总

背景与挑战

背景概述

Statlog项目数据集是一个多领域的数据集集合，包含车辆轮廓、卫星图像、信用审批等多个子集，用于比较不同算法在工业应用中的性能。数据集由欧洲Statlog项目使用，涵盖医学、金融等多个真实世界场景。