Abstract：Interactive classification is one of the main means schemes to solve complex classification problems of data.In most existing interactive classification system,users can accurately identify the categories of data.However,in some classification scenarios,there is an order relationship between categories,and it is easier for users to identify the order relationship. This paper presented an interactive data classification method supported by sorting.It introduces the user's cognition of the data order information to improve the accuracy of interactive classification with a candidate sample recommendation strategy to reduce users' difficulty of identifying.To demonstrate the potential of the new approach, we propose a visualization method for classification algorithm performance measurement based on the vehicle data set of accident information including basic vehicle information,illegal driving information and vehicle traffic records.The relevant vehicles are divided into high-risk vehicles,medium-risk vehicles,and low-risk vehicles.The classification consistency reaches nearly 98%,which verifies the effectiveness of the proposed method.
邓惠俊. 排序支持的交互数据分类算法及其应用[J]. 浙江大学学报（理学版）, 2021, 48(1): 9-17.
DENG Huijun. Ranking-supported interactive data classification method and its application. Journal of ZheJIang University(Science Edition), 2021, 48(1): 9-17.
1 PAIVA J G S，SCHWARTZ W R，PEDRINI H，et al.An approach to supporting incremental visual data classification［J］.IEEE Transactions on Visualization and Computer Graphics，2014，21（1）：4-17. DOI：10.1109/TVCG.2014.2331979 2 马昱欣，曹震东，陈为.可视化驱动的交互式数据挖掘方法综述［J］.计算机辅助设计与图形学学报，2016，28（1）：1-8.DOI：10.3969/j.issn.1003-9775. 2016.01.001 MA Y X，CAO Z D，CHEN W.A survey of visualization-driven interactive data mining approaches［J］.Journal of Computer-Aided Design，2016，28（1）：1-8.DOI：10.3969/j.issn.1003-9775.2016. 01.001 3 SACHA D，SEDLMAIR M，ZHANG L，et al.What you see is what you can change：Human-centered machine learning by interactive visualization［J］.Neurocomputing，2017，268：164-175.DOI：10.1016/j.neucom.2017.01.105 4 MING Y，QU H，BERTINI E.RuleMatrix：Visualizing and understanding classifiers with rules［J］.IEEE Transactions on Visualization and Computer Graphics，2018，25（1）：342-352.DOI：10.1109/TVCG.2018.2864812 5 AFFONSO C，ROSSI A L D，VIEIRA F H A，et al.Deep learning for biological image classification［J］.Expert Systems with Applications，2017，85：114-122. DOI：10.1016/j.eswa.2017.05.039 6 KUMAR P，GUPTA A.Active learning query strategies for classification，regression，and clustering：A survey［J］.Journal of Computer Science and Technology，2020，35（4）：913-945.DOI：10.1007/s11390-020-9487-4 7 JOSHI A J，PORIKLI F，PAPANIKOLOPOULOS N P.Scalable active learning for multiclass image classification［J］.IEEE Transactions on Pattern Analysis and Machine Intelligence，2012，34（11）：2259-2273. DOI：10.1109/tpami.2012.21 8 BELUCH W H，GENEWEIN T，N?RNBERGER A，et al.The power of ensembles for active learning in image classification［C］//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Salt Lake City：IEEE，2018：9368-9377. 9 DO T N.Towards Simple，Easy to Understand，An Interactive Decision Tree Algorithm［R］.Can Tho：Can Tho University，2007. 10 VAN DEN ELZEN S，VAN WIJK J J.BaobabView：Interactive construction and analysis of decision trees［C］//2011 IEEE Conference on Visual Analytics Science and Technology （VAST）. Providence：IEEE，2011：151-160. 11 CHOO J，LEE H，KIHM J，et al.iVisClassifier：An interactive visual analytics system for classification based on supervised dimension reduction［C］//2010 IEEE Symposium on Visual Analytics Science and Technology.Salt Lake City：IEEE，2010：27-34.DOI：10.1109/vast.2010.5652443 12 LEGG P A，CHUNG D H S，PARRY M L，et al.Transformation of an uncertain video search pipeline to a sketch-based visual analytics loop［J］.IEEE Transactions on Visualization and Computer Graphics，2013，19（12）：2109-2118.DOI：10.1109/tvcg.2013.207 13 H?FERLIN B，NETZEL R，H?FERLIN M，et al.Interactive learning of Ad-hoc classifiers for video visual analytics［C］//2012 IEEE Conference on Visual Analytics Science and Technology （VAST）.Seattle：IEEE，2012：23-32. 14 MAATEN L，HINTON G.Visualizing data using t-SNE［J］.Journal of Machine Learning Research，2008，9：2579-2605. DOI：10.1007/s10846-008-9235-4 15 RAO R，CARD S K.The Table Lens：Merging graphical and symbolic representations in an interactive focus+ context visualization for tabular information［C］//Proceedings of the ACM SIGCHI Conference on Human Factors in Computing Systems.Boston：ACM，1994：318-322. DOI：10.1145/191666.191776 16 CARENINI G，LOYD J.ValueCharts：Analyzing linear models expressing preferences and evaluations［C］//Proceedings of the Working Conference on Advanced Visual Interfaces.Gallipoli：ACM，2004：150-157. 17 GRATZL S，LEX A，GEHLENBORG N，et al.LineUp：Visual analysis of multi-attribute rankings［J］.IEEE Transactions on Visualization and Computer Graphics，2013，19（12）：2277-2286.DOI：10.1109/TVCG.2013.173 18 VUILLEMOT R，PERIN C.Investigating the direct manipulation of ranking tables for time navigation［C］//Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems.Seoul：ACM，2015：2703-2706.DOI：10.1145/2702123.2702237 19 DI SCIASCIO C，SABOL V，VEAS E E.Rank as you go：User-driven exploration of search results［C］//Proceedings of the 21st International Conference on Intelligent User Interfaces.Sonoma：ACM，2016：118-129. 20 COSSOCK D，ZHANG T.Subset ranking using regression［C］//International Conference on Computational Learning Theory.Heidelberg：Springer，2006：605-619. DOI：10.1007/1177 6420_44 21 WALL E，DAS S，CHAWLA R，et al.Podium：Ranking data using mixed-initiative visual analytics［J］.IEEE Transactions on Visualization and Computer Graphics，2017，24（1）：288-297.DOI：10.1109/TVCG.2017.2745078 22 WANG Y，FENG K，CHU X，et al.A perception-driven approach to supervised dimensionality reduction for visualization［J］.IEEE Transactions on Visualization and Computer Graphics，2017，24（5）：1828-1840. DOI：10.1007/978-3-319-12610-4_3 23 LAWAL I A.Incremental SVM learning［C］//Learning from Data Streams in Evolving Environments. Cham：Springer，2019：279-296. 24 JOACHIMS T.Training linear SVMs in linear time［C］//Proceedings of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining.Philadelphia：ACM，2006：217-226.DOI：10.1145/1150402.1150429 25 JOACHIMS T.Supported vector machine for ranking［EB/OL］.［2009-03-21］.http：//www.cs.cornell.edu/people/tj/svm_light/svm_rank.html ？tdsourcetag=s_pcqq_aiomsg.
2021, Vol. 48 
