In the paper Figure 7, we claimed: “the best value for R depends on the amount of training data available.” Here are the results for Gun-Point Dataset.

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Presentation transcript:

In the paper Figure 7, we claimed: “the best value for R depends on the amount of training data available.” Here are the results for Gun-Point Dataset and another dataset, Two_ Pat, which we randomly pick half size instances repeatedly. The observation is that with fewer objects in the dataset, the accuracy decreases and peaks at larger window size. Gun PointTwo_Pat

Name# class# features# instancesEvaluationData type JF2220,0002,000/18,000real Letter261620,0005,000/15,000mixed Pen Digits101610,9927,494/3,498real Forest Cover Type754581,01211,340/569,672real Iris fold CVreal Ionosphere fold CVreal Voting Records fold CVBoolean Australian Credit fold CV6 numerial/8 categorical German Credit2241,00010-fold CVreal Leaf /242time series Two_Pat41285,0001,000/4,000time series Face161312,2311,113/1,118time series In the paper Table 4, we list the datasets used in the paper, here we present additional datasets and show all the experiments that could not fit in the paper due the limit of space.

Number of instances seen before interruption, S accuracy(%) Random Train Random Test SimpleRank Train SimpleRank Test Number of instances seen before interruption, S accuracy(%) Random Test SimpleRank Test JF, 2 classed, 20,000 instances, 2,000/18,000

Number of instances seen before interruption, S accuracy(%) Random Train Random Test SimpleRank Train SimpleRank Test Letter, 26 classes, 20,000 instances, 5,000/15, Number of instances seen before interruption, S accuracy(%) Random Test SimpleRank Test

Number of instances seen before interruption, S accuracy(%) Random Train Random Test SimpleRank Train SimpleRank Test Number of instances seen before interruption, S accuracy(%) Random Test SimpleRank Test Pen digits, 10 classed, 10,992 instances, 7,494/3,498

Number of instances seen before interruption, S accuracy(%) Random Train Random Test SimpleRank Train SimpleRank Test Number of instances seen before interruption, S accuracy(%) Random Test SimpleRank Test Forest Cover Type, 7 classes, 581,012 instances, 11,340/569,672

Number of instances seen before interruption, S accuracy(%) Random Test SimpleRank Test Australian Credit, 2 classes, 690 instances, 10-fold Cross Validation Number of instances seen before interruption, S data instances accuracy(%) RandomTrain RandomTest SimpleRankTrain SimpleRankTest DROP1 DROP2 DROP3

Number of instances seen before interruption, S accuracy(%) Random Train Random Test SimpleRank Train SimpleRank Test Number of instances seen before interruption, S accuracy(%) Random Test SimpleRank Test Number of instances seen before interruption, S accuracy(%) Random Test SimpleRank Test BestDrop Test data instances accuracy(%) RandomTrain RandomTest SimpleRankTrain SimpleRankTest DROP1 DROP2 DROP3 Ionosphere, 2 classes, 351 instances, 10-fold Cross Validation

Iris, 3 classes, 150 instances, 10-fold Cross Validation Number of instances seen before interruption, S accuracy(%) Random Train Random Test SimpleRank Train SimpleRank Test Number of instances seen before interruption, S accuracy(%) Random Test SimpleRank Test data instances accuracy(%) RandomTrain RandomTest SimpleRankTrain SimpleRankTest DROP1 DROP2 DROP3

Number of instances seen before interruption, S accuracy(%) Random Train Random Test SimpleRank Train SimpleRank Test Number of instances seen before interruption, S accuracy(%) Random Test SimpleRank Test Voting records Number of instances seen before interruption, S accuracy(%) Random Test SimpleRank Test BestDrop Test data instances accuracy(%) RandomTrain RandomTest SimpleRankTrain SimpleRankTest DROP1 DROP2 DROP3

Number of instances seen before interruption, S accuracy(%) Random Train Random Test SimpleRank Train SimpleRank Test Number of instances seen before interruption, S accuracy(%) Random Test SimpleRank Test German Credit, 2 classes, 1,000 instances, 10-fold Cross Validation data instances accuracy(%) RandomTrain RandomTest SimpleRankTrain SimpleRankTest DROP1 DROP2 DROP3

Two_Pat, 4 classes, 5,000 instances, 1,000/4,000 split

Leaf Dataset, 6 classes, 442 instances

Number of instances seen before interruption, accuracy(%) Face Dataset Random, Euclidean distance Random, Fixed R = 4 SimpleRank, Fixed R = 4 SimpleRank, AdaptiveR Random, Euclidean distance Random, Fixed R = 3 SimpleRank, Fixed R = 3 SimpleRank, AdaptiveR 3% 4% Face dataset, 16 classes, 2,231 instances, 1,113/1,118 split