pruning1100lu.com

1100lu.com  时间:2021-03-22  阅读:()
[Typetext][Typetext][Typetext]2014TradeScienceInc.
ISSN:0974-7435Volume10Issue24BioTechnologyAnIndianJournalFULLPAPERBTAIJ,10(24),2014[16338-16346]ApplicationresearchofdecisiontreealgorithminenglishgradeanalysisZhaoKunBeihuaUniversity,Teacher'scollege,Jilin,(CHINA)ABSTRACTThispaperintroducesandanalysesthedatamininginthemanagementofstudents'grades.
Weusethedecisiontreeinanalysisofgradesandinvestigateattributeselectionmeasureincludingdatacleaning.
WetakecoursescoreofinstituteofEnglishlanguageforexampleandproducedecisiontreeusingID3algorithmwhichgivesthedetailedcalculationprocess.
Becausetheoriginalalgorithmlacksterminationcondition,weproposeanimprovedalgorithmwhichcanhelpustofindthelatencyfactorwhichimpactsthegrades.
KEYWORDSDecisiontreealgorithm;Englishgradeanalysis;ID3algorithm;Classification.
BTAIJ,10(24)2014ZhaoKun16339INTRODUCTIONWiththerapiddevelopmentofhighereducation,EnglishgradeanalysisasanimportantguaranteeforthescientificmanagementconstitutesthemainpartoftheEnglisheducationalassessment.
Theresearchonapplicationofdatamininginmanagementofstudents'gradeswantstotalkhowtogettheusefuluncoveredinformationfromthelargeamountsofdatawiththedataminingandgrademanagement[1-5].
Itintroducesandanalysesthedatamininginthemanagementofstudents'grades.
Itusesthedecisiontreeinanalysisofgrades.
Itdescribesthefunction,statusanddeficiencyofthemanagementofstudents'grades.
Ittellsushowtoemploythedecisiontreeinmanagementofstudents'grades.
ItimprovestheID3arithmetictoanalyzethestudents'gradessothatwecouldfindthelatencyfactorwhichimpactsthegrades.
Ifwefindoutthefactors,wecanofferthedecision-makinginformationtoteachers.
Italsoadvancesthequalityofteaching[6-10].
TheEnglishgradeanalysishelpsteacherstoimprovetheteachingqualityandprovidesdecisionsforschoolleaders.
Thedecisiontree-basedclassificationmodeliswidelyusedasitsuniqueadvantage.
Firstly,thestructureofthedecisiontreemethodissimpleanditgeneratesruleseasytounderstand.
Secondly,thehighefficiencyofthedecisiontreemodelismoreappropriateforthecaseofalargeamountofdatainthetrainingset.
Furthermorethecomputationofthedecisiontreealgorithmisrelativelynotlarge.
Thedecisiontreemethodusuallydoesnotrequireknowledgeofthetrainingdata,andspecializesinthetreatmentofnon-numericdata.
Finally,thedecisiontreemethodhashighclassificationaccuracy,anditistoidentifycommoncharacteristicsoflibraryobjects,andclassifytheminaccordancewiththeclassificationmodel.
Theoriginaldecisiontreealgorithmusesthetop-downrecursiveway[11-12].
Comparisonofpropertyvaluesisdoneintheinternalnodesofthedecisiontreeandaccordingtothedifferentpropertyvaluesjudgedownbranchesfromthenode.
Wegetconclusionfromthedecisiontreeleafnode.
Therefore,apathfromtheroottotheleafnodecorrespondstoaconjunctiverules,theentiredecisiontreecorrespondstoasetofdisjunctiveexpressionsrules.
Thedecisiontreegenerationalgorithmisdividedintotwosteps[13-15].
Thefirststepisthegenerationofthetree,andatthebeginningallthedataisintherootnode,thendotherecursivedataslice.
Treepruningistoremovesomeofthenoiseorabnormaldata.
Conditionsofdecisiontreetostopsplittingisthatanodedatabelongstothesamecategoryandtherearenotattributesusedtosplitthedata.
Inthenextsection,weintroduceconstructionofdecisiontree.
InSection3weintroduceattributeselectionmeasure.
InSection4,wedoempiricalresearchbasedonID3algorithmandproposeanimprovedalgorithm.
InSection5weconcludethepaperandgivesomeremarks.
CONSTRUCTIONOFDECISIONTREEUSINGID3ThegrowingstepofthedecisiontreeisshowninFigure1.
Decisiontreegenerationalgorithmisdescribedasfollows.
Thenameofthealgorithmis__Generatedecisiontreewhichproduceadecisiontreebygiventrainingdata.
Theinputistrainingsampleswhichisrepresentedwithdiscretevalues.
Candidateattributesetisattribute.
Theoutputisadecisiontree.
Step1.
SetupnodeN.
IfsamplesisinasameclassCthenreturnNasleadnodeandlabelitwithC.
Step2.
Ifattribute_listisempty,thenreturnNasleafnodeandlabelitwiththemostcommonclassinthesamples.
Step3.
Choose_testattributewithinformationgainintheattribute_list,andlabelNas_testattribute.
Step4.
Whileeachiainevery_testattributedothefollowingoperation.
Step5.
NodeNproducesabranchwhichmeetstheconditionof_itestattributeaStep6.
Supposeisissamplesetof_itestattributeainthesamples.
Ifisisempty,thenplusaleafandlabelitasthemostcommonclass.
OtherwiseplusanodewhichwasreturnedbyiGeneratedecisiontreesattributelisttestattribute.
16340ApplicationresearchofdecisiontreealgorithminenglishgradeanalysisBTAIJ,10(24)2014Figure1:GrowingstepofthedecisiontreeANIMPROVEDALGORITHMAttributeselectionmeasureSupposeSisdatasamplesetofsnumberandclasslabelattributehasmdifferentvalues(1,2,,)iCim.
SupposeiSisthenumberofsampleofclassiCinS.
Foragivensampleclassificationthedemandedexpectationinformationisgivenbyformula1[11-12].
1221log(1,2,,,)mjjmjijijiIssKsppiKn(1)12121()VjjmjjjmjjSSSEAISSKSS(2)ipisprobabilitythatrandomsamplebelongstoiCandisestimatedby/iss.
SupposeattributeAhasVdifferentvalues12Vaaa.
WecanuseattributeAtoclassifySintoVnumberofsubset12(,,)VSSS.
SupposeijSisthenumberofclassiCinsubsetjS.
Theexpectedinformationofsubsetisshowninformula2.
12()jjmjSSSSistheweightofthej-thsubset.
ForagivensubsetjSformula3setsup[13].
1221log(1,2,,,)mjjmjijijiIssKsppiKn(3)BTAIJ,10(24)2014ZhaoKun16341ijijjspsistheprobabilitythatsamplesofjsbelongstoclassiC.
IfwebranchinA,theinformationgainisshowninformula4[14].
12mGainAIsssEA(4)TheimprovedalgorithmTheimprovedalgorithmisasfollows.
Function__Generatedecisiontree(trainingsamples,candidateattributeattribute_list){SetupnodeN;IfsamplesareinthesameclassCthenReturnNasleafnodeandlabelitwithC;Recordstatisticaldatameetingtheconditionsontheleafnode;Ifattribute_listisemptythenReturnNastheleafnodeandlabelitasthemostcommonclassofsamples;Recordstatisticaldatameetingtheconditionsontheleafnode;SupposeGainMax=max(Gain1,Gain2,…,Gainn)IfGainMax='85'Updatekssetci_pi='medium'whereci_pj>='75'andci_pj='60'andci_pj<'75'Updatekssetsjnd='high'wheresjnd='1'Updatekssetsjnd='medium'wheresjnd='2'Updatekssetsjnd='low'wheresjnd='3'ResultofID3algorithmTABLE2istrainingsetofstudenttestscoressituationinformationafterdatacleaning.
Weclassifythesamplesintothreecategories.
1"outstanding"C,2"medium"C,3"general"C,1300,s21950s,3880s,3130s.
Accordingtoformula1,weobtain123300,1950,880)(300/3130)Isss2/log(300/3130).
22(1950/3130)log(1950/3130)(880/3130)log(880/3130)1.
256003.
Entropyofeveryattributeiscalculatedasfollows.
Firstlycalculatewhetherre-learning.
Foryes,11210s,21950s,31580s.
112131210,950,580)Isss222(210/1740)log(210/1740)(950/1740)log(950/1740)(580/1740)log(580/1740)1.
074901Forno,1290s,221000s,32300s.
12223290,1000,300)Isss222(90/1390)log(90/1390)(1000/1390)log(1000/1390)(300/1390)log(300/1390)1.
373186.
IFsamplesareclassifiedaccordingtowhetherre-learning,theexpectedinformationis1121311222321740/3130)1390/3130)EwhetherrelearningIsssIsss0.
5559111.
0749010.
4440891.
3731861.
240721.
Sotheinformationgainis1230.
015282GainwhetherrelearningIsssEwhetherrelearning.
Secondlycalculatecoursetype,whenitisA,112131110,200,580sss.
112131222110,200,580)(110/890)log(110/890)(200/890)log(200/890)(580/890)log(580/890)Isss1.
259382.
ForcoursetypeB,122232100,400,0sss.
BTAIJ,10(24)2014ZhaoKun1634312223222100,400,0)(100/500)log(100/500)(400/500)log(400/500)0Isss0.
721928.
ForcoursetypeC,1323330,550,0sss.
132333220,550,0)(0/550)log(0/550)(550/500)log(550/500)0Isss1.
168009.
ForcoursetypeD,14243490,800,300sss.
14243422290,800,300)(90/1190)log(90/1190)(800/1190)log(800/1190)(300/1190)log(300/1190)Isss1.
168009.
112131122232("")(890/3130)500/3130)EcoursetypeIsssIsss132333142434(550/3130)1190/3130)0.
91749.
IsssIsss("")1.
2560030.
917490.
338513Gaincoursetype.
Thirdlycalculatepaperdifficulty.
Forhigh,112131110,900,280sss.
112131222110,900,280)(110/1290)log(110/1290)(900/1290)log(900/1290)(280/1290)log(280/1290)Isss1.
14385.
Formedium,122232190,700,300sss.
122232222190,700,300)(190/1190)log(190/1190)(700/1190)log(700/1190)(300/1190)log(300/1190)Isss1.
374086Forlow,1323330,350,300sss.
1323332220,350,300)(0/650)log(0/650)(350/650)log(350/650)(300/650)log(300/650)0.
995727.
Isss112131122232("")(1290/3130)1190/3130)EpaperdifficultyIsssIsss132333(650/3130)1.
200512.
Isss("")1.
2560031.
2005120.
55497.
GainpaperdifficultyFourthlycalculatewhetherrequiredcourse.
Foryes,112131210,850,600sss16344ApplicationresearchofdecisiontreealgorithminenglishgradeanalysisBTAIJ,10(24)2014112131222210,850,600)(210/1660)log(210/1660)(850/1660)log(850/1660)(600/1660)log(600/1660)Isss1.
220681.
Forno,12223290,1100,280sss12223222290,1100,280)(90/1470)log(90/1470)(1100/1470)log(1100/1470)(280/1470)log(280/1470)Isss1.
015442.
112131122232("")(1660/3130)1470/3130)1.
220681.
EwhetherrequiredIsssIsss("")1.
2560031.
2206810.
035322.
GainwhetherrequiredTABLE2:TrainingsetofstudenttestscoresCoursetypeWhetherre-learningPaperdifficultyWhetherrequiredScoreStatisticaldataDnomediumnooutstanding90Byesmediumyesoutstanding100Ayeshighyesmedium200Dnolownomedium350Cyesmediumyesgeneral300Ayeshighnomedium250Bnohighnomedium300Ayeshighyesoutstanding110Dyesmediumyesmedium500Dnolowyesgeneral300Ayeshighnogeneral280Bnohighyesmedium150Cnomediumnomedium200ResultofimprovedalgorithmTheoriginalalgorithmlacksterminationcondition.
ThereareonlytworecordsforasubtreetobeclassifiedwhichisshowninTABLE3.
TABLE3:SpecialcaseforclassificationofthesubtreeCoursetypeWhetherre-learningPaperdifficultyWhetherrequiredScoreStatisticaldataAnohighyesmedium15Anohighyesgeneral20BTAIJ,10(24)2014ZhaoKun16345Figure2:DecisiontreeusingimprovedalgorithmAllGainscalculatedare0.
00,andGainMax=0.
00whichdoesnotconformtorecursiveterminationconditionoftheoriginalalgorithminTABLE3.
Thetreeobtainedisnotreasonable,soweadopttheimprovedalgorithmanddecisiontreeusingimprovedalgorithmisshowninFigure2.
CONCLUSIONSInthispaperwestudyconstructionofdecisiontreeandattributeselectionmeasure.
Becausetheoriginalalgorithmlacksterminationcondition,weproposeanimprovedalgorithm.
WetakecoursescoreofinstituteofEnglishlanguageforexampleandwecouldfindthelatencyfactorwhichimpactsthegrades.
REFERENCES[1]XueleiXu,ChunweiLou;"ApplyingDecisionTreeAlgorithmsinEnglishVocabularyTestItemSelection",IJACT:InternationalJournalofAdvancementsinComputingTechnology,4(4),165-173(2012).
[2]HuaweiZhang;"LazyDecisionTreeMethodforDistributedPrivacyPreservingDataMining",IJACT:InternationalJournalofAdvancementsinComputingTechnology,4(14),458-465(2012).
[3]Xin-huaZhu,Jin-lingZhang,Jiang-taoLu;"AnEducationDecisionSupportSystemBasedonDataMiningTechnology",JDCTA:InternationalJournalofDigitalContentTechnologyanditsApplications,6(23),354-363(2012).
[4]ZhenLiu,XianFengYang;"Anapplicationmodeloffuzzyclusteringanalysisanddecisiontreealgorithmsinbuildingwebmining",JDCTA:InternationalJournalofDigitalContentTechnologyanditsApplications,6(23),492-500(2012).
[5]Guang-xianJi;"Theresearchofdecisiontreelearningalgorithmintechnologyofdataminingclassification",JCIT:JournalofConvergenceInformationTechnology,7(10),216-223(2012).
[6]FuxianHuang;"ResearchofanAlgorithmforGeneratingCost-SensitiveDecisionTreeBasedonAttributeSignificance",JDCTA:InternationalJournalofDigitalContentTechnologyanditsApplications,6(12),308-316(2012).
[7]M.
SudheepElayidom,SumamMaryIdikkula,JosephAlexander;"DesignandPerformanceanalysisofDataminingtechniquesBasedonDecisiontreesandNaiveBayesclassifierFor",JCIT:JournalofConvergenceInformationTechnology,6(5),89-98(2011).
[8]MarjanBahrololum,ElhamSalahi,MahmoudKhaleghi;"AnImprovedIntrusionDetectionTechniquebasedontwoStrategiesUsingDecisionTreeandNeuralNetwork",JCIT:JournalofConvergenceInformationTechnology,4(4),96-101(2009).
[9]Bor-tyngWang,Tian-WeiSheu,Jung-ChinLiang,Jian-WeiTzeng,NagaiMasatake;"TheStudyofSoftComputingontheFieldofEnglishEducation:ApplyingGreyS-PChartinEnglishWritingAssessment",JDCTA:InternationalJournalofDigitalContentTechnologyanditsApplications,5(9),379-388(2011).
[10]MohamadFarhanMohamadMohsin,MohdHelmyAbdWahab,MohdFairuzZaiyadi,CikFazilahHibadullah;"AnInvestigationintoInfluenceFactorofStudentProgrammingGradeUsingAssociationRuleMining",AISS:AdvancesinInformationSciencesandServiceSciences,2(2),19-27(2010).
16346ApplicationresearchofdecisiontreealgorithminenglishgradeanalysisBTAIJ,10(24)2014[11]HaoXin;"AssessmentandAnalysisofHierarchicalandProgressiveBilingualEnglishEducationBasedonNeuro-Fuzzyapproach",AISS:AdvancesinInformationSciencesandServiceSciences,5(1),269-276(2013).
[12]Hong-chaoChen,Jin-lingZhang,Ya-qiongDeng;"ApplicationofMixed-Weighted-Association-Rules-BasedDataMiningTechnologyinCollegeExaminationgradesAnalysis",JDCTA:InternationalJournalofDigitalContentTechnologyanditsApplications,6(10),336-344(2012).
[13]YuanWang,LanZheng;"EndocrineHormonesAssociationRulesMiningBasedonImprovedAprioriAlgorithm",JCIT:JournalofConvergenceInformationTechnology,7(7),72-82(2012).
[14]TianBai,JinchaoJi,ZheWang,ChunguangZhou;"ApplicationofaGlobalCategoricalDataClusteringMethodinMedicalDataAnalysis",AISS:AdvancesinInformationSciencesandServiceSciences,4(7),182-190(2012).
[15]HongYanMei,YanWang,JunZhou;"DecisionRulesExtractionBasedonNecessaryandSufficientStrengthandClassificationAlgorithm",AISS:AdvancesinInformationSciencesandServiceSciences,4(14),441-449(2012).
[16]LiuYong;"TheBuildingofDataMiningSystemsbasedonTransactionDataMiningLanguageusingJava",JDCTA:InternationalJournalofDigitalContentTechnologyanditsApplications,6(14),298-305(2012).

CloudCone中国春节优惠活动限定指定注册时间年付VPS主机$13.5

CloudCone 商家产品还是比较有特点的,支持随时的删除机器按时间计费模式,类似什么熟悉的Vultr、Linode、DO等服务商,但是也有不足之处就在于机房太少。商家的活动也是经常有的,比如这次中国春节期间商家也是有提供活动,比如有限定指定时间段之前注册的用户可以享受年付优惠VPS主机,比如年付13.5美元。1、CloudCone新年礼物限定款仅限2019年注册优惠购买,活动开始时间:1月31...

BuyVM迈阿密KVM上线,AMD Ryzen 3900X+NVMe硬盘$2/月起

BuyVM在昨天宣布上线了第四个数据中心产品:迈阿密,基于KVM架构的VPS主机,采用AMD Ryzen 3900X CPU,DDR4内存,NVMe硬盘,1Gbps带宽,不限制流量方式,最低$2/月起,支持Linux或者Windows操作系统。这是一家成立于2010年的国外主机商,提供基于KVM架构的VPS产品,数据中心除了新上的迈阿密外还包括美国拉斯维加斯、新泽西和卢森堡等,主机均为1Gbps带...

1C2G5M轻量服务器48元/年,2C4G8M三年仅198元,COM域名首年1元起

腾讯云双十一活动已于今天正式开启了,多重优惠享不停,首购服务器低至0.4折,比如1C2G5M轻量应用服务器仅48元/年起,2C4G8M也仅70元/年起;个人及企业用户还可以一键领取3500-7000元满减券,用于支付新购、续费、升级等各项账单;企业用户还可以以首年1年的价格注册.COM域名。活动页面:https://cloud.tencent.com/act/double11我们分享的信息仍然以秒...

1100lu.com为你推荐
渣渣辉商标什么是渣渣灰?8080端口路由器要怎么设置才能使外网访问80;8080端口美国互联网瘫痪美国网络大瘫痪到底是怎么发生的newworldNew World Group是什么组织地图应用谁知道什么地图软件好用,求 最好可以看到路上行人同ip网站查询怎么查自己的服务器挂着哪些网站冯媛甑冯媛甄 康熙来了百度关键词工具如何利用百度关键词推荐工具选取关键词百度关键词分析如何正确分析关键词?789se.comhttp://gv789.com/index.php这个网站可信吗?是真的还是假的!
北京租服务器 免费vps 香港vps主机 怎么申请域名 adman 英语简历模板word 鲜果阅读 xen 河南服务器 骨干网络 有奖调查 合租空间 adroit qq对话框 河南移动网 七夕快乐英语 路由跟踪 东莞服务器托管 广州虚拟主机 群英网络 更多