Classification results for: Hellinger Distance Trees for Imbalanced Streams

Data sets supporting the results reported in the paper: Hellinger Distance Trees for Imbalanced Streams, R. J. Lyon, J.M. Brooke, J.D. Knowles, B.W Stappers, 22nd International Conference on Pattern Recognition (ICPR), p.1969 - 1974, 2014. DOI: 10.1109/ICPR.2014.344 Contained in this distribution are results of stream classifier perfromance on four different data sets. Also included are the test results from our attempt at reproducing the outcome of the paper, Learning Decision Trees for Un-balanced Data, D. A. Cieslak and N. V. Chawla, in Machine Learning and Knowledge Discovery in Databases (W. Daelemans, B. Goethals, and K. Morik, eds.), vol. 5211 of LNCS, pp. 241-256, 2008. The data sets used for these experiments include, MAGIC Gamma Telescope Data Set : https://archive.ics.uci.edu/ml/datasets/MAGIC+Gamma+TelescopeMiniBooNE particle identification Data Set : https://archive.ics.uci.edu/ml/datasets/MiniBooNE+particle+identificationSkin Segmentation Data Set : https://archive.ics.uci.edu/ml/datasets/Skin+SegmentationLetter Recognition Data Set : https://archive.ics.uci.edu/ml/datasets/Letter+RecognitionPen-Based Recognition of Handwritten Digits Data Set : https://archive.ics.uci.edu/ml/datasets/Pen-Based+Recognition+of+Handwritten+DigitsStatlog (Landsat Satellite) Data Set : https://archive.ics.uci.edu/ml/datasets/Statlog+(Landsat+Satellite)Statlog (Image Segmentation) Data Set : https://archive.ics.uci.edu/ml/datasets/Statlog+(Image+Segmentation) A further data set used is not publicly available at present. However we are in the process of releasing it for public use. Please get in touch if you'd like to use it. A readme file accompanies the data describing it in more detail.

本数据集用于支撑论文《用于不平衡数据流的赫尔利格距离树（Hellinger Distance Trees for Imbalanced Streams）》的研究成果，作者为R. J. Lyon、J.M. Brooke、J.D. Knowles与B.W. Stappers，发表于第22届国际模式识别会议（22nd International Conference on Pattern Recognition, ICPR），页码1969-1974，2014年，DOI：10.1109/ICPR.2014.344。
本分发包中包含四类数据集上的流式分类器性能测试结果，同时收录了我们复现论文《面向不平衡数据的决策树学习（Learning Decision Trees for Un-balanced Data）》所得到的测试结果。该论文作者为D. A. Cieslak与N. V. Chawla，收录于《机器学习与数据库中的知识发现（Machine Learning and Knowledge Discovery in Databases）》（W. Daelemans、B. Goethals、K. Morik 主编），系列为《计算机科学讲义（Lecture Notes in Computer Science, LNCS）》第5211卷，页码241-256，2008年。
本实验所用数据集如下：
1. MAGIC伽马望远镜数据集（MAGIC Gamma Telescope Data Set）：https://archive.ics.uci.edu/ml/datasets/MAGIC+Gamma+Telescope
2. MiniBooNE粒子识别数据集（MiniBooNE particle identification Data Set）：https://archive.ics.uci.edu/ml/datasets/MiniBooNE+particle+identification
3. 皮肤分割数据集（Skin Segmentation Data Set）：https://archive.ics.uci.edu/ml/datasets/Skin+Segmentation
4. 字母识别数据集（Letter Recognition Data Set）：https://archive.ics.uci.edu/ml/datasets/Letter+Recognition
5. 手写数字笔式识别数据集（Pen-Based Recognition of Handwritten Digits Data Set）：https://archive.ics.uci.edu/ml/datasets/Pen-Based+Recognition+of+Handwritten+Digits
6. Statlog（陆地卫星）数据集（Statlog (Landsat Satellite) Data Set）：https://archive.ics.uci.edu/ml/datasets/Statlog+(Landsat+Satellite)
7. Statlog（图像分割）数据集（Statlog (Image Segmentation) Data Set）：https://archive.ics.uci.edu/ml/datasets/Statlog+(Image+Segmentation)
另有一个数据集目前尚未公开，但我们正在推进其公开发布流程，若您有使用需求可与我们取得联系。本数据包附带README文件，可对相关数据集进行更详细的说明。