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1 Active Mining of Data Streams Wei Fan, Yi-an Huang, Haixun Wang and Philip S. Yu Proc. SIAM International Conference on Data Mining 2004 Speaker: Pei-Min Chou Date:2005/01/14

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2 Introduction In most real-world problems, labelled data streams are rarely immediately available models are refreshed periodically we propose a new concept of demand-driven active data mining.

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3 Method Step1:Detect potential changes of data streams --- ” Guess ” Step2:If guessed loss or error rate higher than tolerable maximum--- choose a small number of data records Step3:If statistically estimated loss higher than tolerable maximum--- Reconstruct the old model

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4 Definition(1) D c :complete data set D:training set S:data stream dt:Decision tree constructed from D Tolerable Maximum: Exact values are completely defined by each application

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5 Definition(2) n l :number of instance classified by leaf l N:size of data stream Statistic at leaf l Σp( l )=1

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6 Example Name Banklocalprice MaryICEA500 JohnIAEB700 BillyICEA100 EllaICEB300 BobIDEC500 PaulIBEB700 TomICEA100 AmyIBEB700 Name Banklocalpriceclass MaryICEA500C2 JohnIAEB700C4 BillyICEA100C1 EllaICEB300C3 PaulIBEB700C6 TomICEA100C1 AmyIBEB700C6 D:training set Dc:complete set

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7 Example---decision tree Bank is ICE Local is ABank is IBE Price is 100 Local is B C1: Billy Tom C2: Mary C3: Ella C4: John C6: Paul Amy yes no P D ( l )=2/7 1/7 2/7 C5 0 yes

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8 Observable Statistics(1) p s ( l ):statistic at leaf l in S p D ( l ): statistic at leaf l in D Change of leaf statistic on data stream PS means that significant change occur

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9 Example(2) Name Banklocalprice ErinICEA500 JoJoIAEB700 BossIBEC500 HebeICEA500 SamIBEC500 Bank is ICE Local is ABank is IBE Price is 100Local is B C1C2: Erin Hebe C4: Boss Sam C5: JoJo C6 yes no yes no P s ( l )=0 2/5 1/5 0 S: New data stream C3 0

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10 Observable Statistics(2) L a :validation loss L e :sum of expected loss at every leaf LS:potential change in loss due to changes in the data stream Difference :LS take the loss function into account

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11 Example(3) Name Banklocalprice HebeICEA- SamIBEC700 Bank is ICE Local is ABank is IBE Price is 100Local is B C1C2C4: Boss Sam C5: JoJo C6: yes no yes no S: New data stream C3 Major 0.7 L e(C2)=(1-0.7)*30%=9% ErinHebe 30%

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12 Loss Estimation When two statistics above tolerable maximum occur Investigate true class labels of a selected number of example Assume loss of each example:{l 1. l 2. l 3…. l n } Average loss : Σl i/n Standard error: ( ) Investigation cost :not for free

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13 Experiment(1) Changing statistics is good indicator of change

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14 Experiment(2)

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15 Experiment(3)

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16 Experiment(4)

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17 Experiment---Result Two statistics are very well correlated with the amount of change Statistically estimated loss range is very close to true value

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18 Conclusion Estimates the error without knowing the true class labels statistical sampling method to estimate the range of true loss Model reconstruction whenever estimated loss is higher than tolerable maximum.

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