SlowGuess/NeurIPS_2023_OCR huggingface dataset:
Our experiment contains 162 datasets. It would be time-consuming to perform hyperparameter selection for each dataset. Therefore, the hyperparameters of Scale-teaching are not carefully tuned for each dataset, and most of the hyperparameters are set based on the default hyperparameters of related works. The learning rate and maximum epoch are set based on the parameters of existing noise-label learning methods, such as FINE and CULCU.  $\alpha$  in Eq. 3,  $\sigma$  in Eq. 4 and  $\beta$  in Eq. 5 are set based on the default hyperparameters of related label propagation works.  $e_{warm}$  is based on FINE settings.  $e_{update}$ ,  $\gamma$  and batch size are based on manual empirical settings without specific hyperparameter analysis. The largest neighbor  $K$  is set based on human experience, and we had a simple test on several datasets, and found that a larger value of does not improve the classification performance, but instead increases the running time of the model.
For the implementation of small-loss criterion in Scale-teaching, we select small-loss samples within each class from the mini-batch data as clean labeled data. For stduies [12, 13], they use warm-up training to decrease  $\lambda(e)$  from 1 to  $1 - \eta$ .  $\lambda(e)$  denotes the selection ratio of small-loss samples within the mini-batch data without considering the difference of class, and  $\eta$  is the ratio of noise labels in the training set. Based on the above criterion, the current work [29] uses the Jensen-Shannon divergence to calculate difference  $d$  between the classification result  $p_i$  of sample  $\mathcal{X}_i^c$  and the observation label  $\widehat{y}_i$ . Following [29], for each class  $c$ , we consider the observed label of  $\mathcal{X}_i^c$  as a clean label when the  $d$  of the training sample  $\mathcal{X}_i^c$  is less than  $d_{avg}^e$  after a  $e_{warm}$  warm-up training.  $d_{avg}^e$  denotes the average of  $ds$  of all the training samples when the epoch is  $e$ . We observed that using the Jensen-Shannon divergence method [29] and directly employing stduies [12, 13] for clean sample selection within each class have distinct strengths and weaknesses when applied to various time series datasets. In our study, we implemented the strategy of stduies [12, 13] for clean sample selection within each class on four individual large datasets and the UCR 128 archive. Meanwhile, the Jensen-Shannon divergence method [29] was applied to the UEA 30 archive for clean sample selection within each class.
D Details of Main Results
For the four individual large time series datasets, the specific classification results of our proposed Scale-teaching paradigm and baselines are shown in Table 6. For the UCR 128 archive, the specific classification results for all methods with different noise ratios are shown in Table 11 (Sym  $20\%$ ), 12 (Sym  $50\%$ ), 13 (Asym  $40\%$ ), and 14 (Ins  $40\%$ ). For the UEA 30 archive, the specific classification results for all methods at different noise ratios are shown in Tables 15 (Sym  $20\%$ ), 16 (Sym  $50\%$ ), 17 (Asym  $40\%$ ), and 18 (Ins  $40\%$ ). For layout and reading convenience, we only give the average classification accuracy for multiple runs of all methods without standard deviation on the UCR 128 archive and UEA 30 archive.
E Details of Multi-scale Results
To analyze the multi-scale mechanism in the Scale-teaching paradigm, we provide the classification performance of classifiers corresponding to fine, medium and coarse scales, as shown in Tables 19 and 21. And the classification results by ablation cross-scale fusion mechanism based on the Scale-teaching are shown in Tables 20 and 22. For the abbreviations in Tables 19, 20, 21 and 22, such as a_t_b_f, b_f_c_t, and c_t_a_f, where  $a$  denotes fine classifier,  $b$  denotes medium classifier, and  $c$  denotes coarse classifier, and  $t$  and  $f$  represent correct and incorrect classification results, respectively. For example, a_t_b_f indicates the number of samples correctly predicted by the fine classifier and incorrectly predicted by the medium classifier. In addition, we provide t-SNE [59] visualization on the FD-A dataset with Sym  $50\%$  noisy labels (as in Figure 6) to explore the distribution of different classes of embeddings. Figure 6 shows that the cross-scale fusion mechanism in Scale-teaching for
Algorithm 1 The proposed Scale-teaching paradigm.
Input: encoders  $[w_A, w_B, w_C]$ , classifiers  $[c_A, c_B, c_C]$ , fine-scale series  $x_A$ , medium-scale series  $x_B$ , and coarse-scale series  $x_C$
Output:  $[w_A, w_B, w_C]$  and  $[c_A, c_B, c_C]$
Note: For clarity, our analysis utilizes three distinct scales for training, but this approach can be extended to incorporate multiple scales.
1: Step one: Obtain single-scale embeddings  $r_A, r_B, r_C$ ;
$$
r _ {A} = w _ {A} (x _ {A});
$$
$$
r _ {B} = w _ {B} (x _ {B});
$$
$$
r _ {C} = w _ {C} (x _ {C});
$$
2: Step two: Obtain cross-scale embeddings  $v_{A}, v_{B}, v_{C}$ ;
$$
v _ {A} = r _ {A};
$$
$$
v _ {B} = \operatorname {E q.} 2 \left(r _ {B}, v _ {A}\right);
$$
$$
v _ {C} = \operatorname {E q.} 2 \left(r _ {C}, v _ {B}\right);
$$
3: Step three: Obtain clean labels  $y_{A}$ ,  $y_{B}$ ,  $y_{C}$  for cross-teaching training;
$y_{A} = c_{C}(v_{C})$  via small loss criterion;
$y_{B} = c_{A}(v_{A})$  via small loss criterion;
$y_{C} = c_{B}(v_{B})$  via small loss criterion;
4: Step four: Obtain corrected labels  $yc_{A}$ ,  $yc_{B}$ ,  $yc_{C}$  for classification training;
$y c_{A} = \mathrm{Eq.} 6(v_{A}, y_{A})$  via label propagation;
$y c_{B} = \mathrm{Eq.} 6(v_{B},y_{B})$  via label propagation;
$y c_{C} = \operatorname{Eq.} 6(v_{C}, y_{C})$  via label propagation;
5: Step five: Overall training;
Update encoder  $w_{A}$  and classifier  $c_{A}$  via cross-entropy loss  $(v_{A}, y_{A} \& yc_{A})$ ;
Update encoder  $w_{B}$  and classifier  $c_{B}$  via cross-entropy loss  $(v_{B}, y_{B} \& yc_{B})$ ;
Update encoder  $w_{C}$  and classifier  $c_{C}$  via cross-entropy loss  $(v_{C}, y_{C} \& yc_{C})$ .
Table 6: The detailed test classification accuracy (\%) compared with baselines on four individual large datasets (values in parentheses are standard deviations). The best results are in bold.
Dataset	Noise	Standard	Mixup	Co-teaching	FINE	SREA	SELC	CULCU	Scale-teaching
HAR	Sym 20%	92.13 (0.64)	92.52 (1.05)	92.28 (0.67)	92.15 (0.55)	92.53 (1.41)	92.88 (0.82)	92.66 (0.37)	93.93 (0.66)
Sym 50%	83.99 (2.89)	76.75 (1.88)	89.90 (1.63)	88.42 (3.83)	91.38 (0.59)	90.37 (0.73)	89.91 (2.19)	90.17 (0.67)
Asym 40%	75.59 (5.39)	66.91 (2.61)	87.67 (2.52)	83.87 (5.98)	88.98 (0.57)	87.67 (2.39)	87.22 (1.22)	89.62 (0.73)
Ins 40%	83.56 (2.82)	73.86 (0.89)	90.98 (0.96)	90.77 (0.33)	91.25 (1.11)	91.02 (1.53)	91.15 (1.43)	91.58 (1.47)
UniMiB-SHAR	Sym 20%	87.07 (0.95)	82.13 (1.08)	80.54 (2.16)	26.63 (3.07)	51.48 (3.65)	68.52 (2.86)	82.80 (1.87)	90.69 (1.02)
Sym 50%	79.37 (0.41)	77.77 (1.59)	66.33 (2.85)	18.92 (4.61)	47.62 (3.33)	67.65 (3.31)	66.36 (3.91)	81.31 (0.67)
Asym 40%	63.59 (4.13)	66.32 (1.93)	60.25 (1.45)	19.18 (4.37)	51.16 (3.01)	55.65 (1.59)	60.45 (1.65)	70.68 (2.15)
Ins 40%	55.83 (8.14)	56.97 (6.48)	54.09 (3.79)	11.18 (4.75)	51.5 (1.98)	54.62 (6.63)	53.90 (4.75)	71.14 (3.99)
FD-A	Sym 20%	98.89 (0.05)	99.78 (0.06)	99.83 (0.08)	78.13 (21.47)	89.92 (0.68)	99.67 (0.09)	99.85 (0.08)	99.93 (0.04)
Sym 50%	96.63 (1.16)	98.73 (0.62)	99.04 (0.32)	70.65 (17.53)	82.18 (0.01)	98.59 (0.25)	99.06 (0.29)	99.38 (0.53)
Asym 40%	96.12 (1.65)	93.50 (1.85)	97.06 (4.05)	61.04 (14.24)	90.23 (0.02)	98.24 (0.58)	98.91 (0.42)	99.55 (0.36)
Ins 40%	99.36 (0.47)	99.55 (0.10)	99.51 (0.19)	67.81 (12.95)	88.63 (0.02)	99.36 (0.23)	99.53 (0.22)	99.82 (0.06)
Sleep-EDF	Sym 20%	85.01 (0.09)	84.31 (0.36)	84.81 (0.14)	81.21 (0.28)	72.79 (0.99)	84.32 (0.33)	85.23 (0.14)	85.56 (0.35)
Sym 50%	83.58 (0.74)	83.61 (0.39)	83.39 (0.25)	78.17 (4.42)	72.78 (1.30)	83.06 (0.29)	84.02 (0.53)	84.59 (0.97)
Asym 40%	79.62 (2.39)	77.40 (1.92)	82.87 (0.40)	64.77 (2.10)	72.23 (0.89)	82.50 (1.07)	83.05 (0.64)	83.87 (0.38)
Ins 40%	84.35 (0.38)	84.25 (0.31)	84.62 (0.28)	79.68 (2.55)	71.99 (1.24)	83.78 (0.28)	84.86 (0.22)	85.03 (0.61)
(a) Standard  $(96.73\%)$
(b) CULCU  $(98.77\%)$
(c) Scale-teaching  $(99.79\%)$
Figure 6: t-SNE visualization of the learned embeddings on the FD-A dataset with Sym  $50\%$  noisy labels (values in parentheses are the test classification accuracies).
(a) HAR Dataset
Figure 7: Multi-scale sampling strategies analysis under Sym  $50\%$  noisy labels.
(b) UniMiB-SHAR Dataset
time-series label-noise learning can make the embeddings of different classes more discriminative, thus facilitating clean sample selection and noisy label correction.
Impact of downsampling scale sequence list. Scale-teaching can be performed using a variety of different downsampling scales for label-noise learning. Based on the experience of [19, 44] on time series classification and prediction tasks, we utilize the downsampling scales of [1,2,4] for the experimental analyses of Scale-teaching. However, for real-world scenarios that actually contain noisy labels, it is generally not possible to perform hyperparametric analyses using a clean-labeled validation set. To facilitate the analysis, in this paper, we use the classification performance of the test set for multi-scale hyperparameter analyses. However, to avoid test set information leakage, we do not use the hyperparameter analysis result for Scale-teaching in our experiments. We use two multi-scale sampling strategies for analyses, which are (1)  $\{[1,2], [1,2,3], [1,2,3,4], [1,2,3,4,5]\}$ ; (2)  $\{[1,3], [1,2,4], [1,2,4,8], [1,2,4,8,16]\}$ . From Figure 7, we find that Scale-teaching using four different scales for training has the highest classification accuracy, which indicates that more input scales do not necessarily make the classification performance better. In addition, using three or four scales of sequences can effectively improve the classification performance of Scale-teaching compared with using two different scales.
Impact of input scales of sequences order. Scale-teaching employs a finer-to-coarser strategy for cross-scale embedding fusion. Intuitively, when a single scale is used for classification, the original single scale (finer) time series is better overall because it does not discard the original sequence information compared to coarser scale time series. Therefore, Scale-teaching is trained using the finer-to-coarser cross-scale fusion strategy. To analyze the difference in classification performance between different fusion directions, we subtract the classification accuracy using the finer-to-coarser and coarser-to-finer training approaches, and the specific results are shown in Figure 8. We can find that the classification performance of finer-to-coarser is better overall, which is due to its ability to use a single fine-scale sequence with an excellent classification performance from the beginning to gradually promote the classification performance of multiscale fusion embeddings.
Figure 8: The cross fusion direction of input scale series analysis under Sym  $50\%$  noisy labels.
(a) Adiac
(b) CricketZ
Figure 9: The change of loss values for clean and noisy time series samples under Aysm  $40\%$  noise labels. The solid line and shading indicate the mean and standard deviation loss values of all clean (or noisy) training samples within each epoch.
F Small-loss Visualization
The small-loss criterion has been extensively validated for clean label selection in label-noise learning for computer vision. To further analyze the application of the small-loss criterion in time series data, we provide the change of loss values of the models trained by the Standard method and Scale-teaching on Adiac and CricketZ UCR datasets (as in Figure 9). Also, we visualize the probability distributions of the ground-truth clean and noisy (corrupted) sample loss values on the test set with different training strategies. Specifically, Figures 10 and 11 show the loss probability distributions of the models trained by different strategies on the HAR dataset and UniMiB-SHAR with Aysm  $40\%$  noisy labels. Both red (clean) and blue (corrupted) in Figure 10 and Figure 11 contain two peaks, which indicate that some correctly labeled samples are still difficult to learn (large loss) and some incorrectly labeled samples are also easy to learn (small loss). Compared with the Standard method (Figure 10 (a) and Figure 11 (a)), Scale-teaching (Figure 10 (b) and Figure 11 (b)) can clearly distinguish clean and noisy samples by the loss value distribution, further validating the robustness of the multi-scale embeddings to cope with time-series noisy labels.
G Other Analysis
The test F1-score results of ablation study. Following [4], we select the averaged F1-score on the test set as a new metric for ablation analysis in Section 4.5. Hence, we give the corresponding test classification F1-score (\%) in Tables 7 and 8.
Running time analysis. We select two datasets for running the time-consuming analysis, the FD-A dataset with the largest sequence length and the Sleep-EDF dataset with the largest samples. We performed the running time statistics on the NVIDIA GeForce RTX 3090 GPU using all baselines, and the results are shown in Table 9. On the FD-A dataset with the longest sequence length, Co-teaching and CULCU take essentially twice as long to run as the Stanard method because they use
(a) Standard
(b) Scale-teaching
Figure 10: The loss value probability distributions visualization on HAR dataset with Asym  $40\%$  noisy labels.
(a) Standard
Figure 11: The loss value probability distributions visualization on UniMiB-SHAR dataset with Asym  $40\%$  noisy labels.
Table 7: The test classification F1-score (\%) results of ablation study (values in parentheses denote drop F1-score).
Method	HAR	UniMiB-SHAR
Sym 50%	Asym 40%	Sym 50%	Asym 40%
Scale-teaching	90.05	89.14	77.56	65.89
w/o cross-scale fusion	88.16 (-1.89)	87.05 (-2.09)	68.23 (-9.33)	57.76 (-8.13)
only single scale	87.56 (-2.49)	86.75 (-2.39)	66.87 (-10.69)	54.12 (-11.77)
w/o graph learning	87.79 (-2.26)	87.41 (-1.73)	74.62 (-2.94)	63.15 (-2.74)
w/o moment	89.34 (-0.71)	88.27 (-0.87)	76.67 (-0.89)	64.92 (-0.97)
w/o dynamic threshold	88.93 (-1.12)	88.29 (-0.85)	73.11 (-4.45)	64.76 (-1.17)
Table 8: The test classification F1-score (\%) results on four individual large datasets without noisy labels. The best results are **bold**, and the second best results are underlined.
Dataset	Standard	Mixup	Co-teaching	FINE	SREA	SELC	CULCU	Scale-teaching
HAR	93.27	95.39	93.75	93.19	92.91	93.71	94.72	94.18
UniMiB-SHAR	86.37	80.17	84.43	84.03	66.54	89.19	86.45	93.62
FD-A	99.93	99.91	99.96	64.05	90.14	99.82	99.95	99.96
Sleep-EDF	81.99	82.11	82.52	83.07	77.67	82.17	83.26	84.76
Table 9: Training time (hours) analysis using the FD-A and Sleep-EDF datasets with Asym  $40\%$  noisy labels.
Dataset	Standard	Mixup	Co-teaching	FINE	SREA	SELC	CULCU	Scale-teaching
[1,2,4]	[1,4,16]	[1,8,32]
FD-A	0.37	0.42	0.79	0.63	0.90	0.42	0.87	1.06	0.86	0.82
Sleep-EDF	0.54	0.83	1.09	2.04	1.64	0.73	1.47	2.02	1.76	1.60
Table 10: Comparison with classification methods without label noise learning strategy. The best test classification accuracy (%) results are bold, and the second best results are underlined.
Dataset	HAR	FD-A
Method	0	Sym 20%	Sym 50%	Asym 40%	Ins 40%	0	Sym 20%	Sym 50%	Asym 40%	Ins 40%
Boss [15]	72.34	62.55	56.11	53.29	52.34	69.75	64.75	57.95	61.99	62.25
Rocket [60]	95.29	92.93	90.04	82.53	90.43	99.99	99.71	97.01	89.75	97.98
FCN [57]	93.74	92.13	83.99	75.59	83.56	99.56	98.89	96.63	96.12	99.36
Scale-teaching	94.72	93.93	90.17	89.62	91.58	99.98	99.93	99.38	99.55	99.82
two encoders. Furthermore, although SREA uses a single network training, it utilizes a decoder for the unsupervised reconstruction task of the original time series, which significantly increases training time on the FD-A dataset with longer sequences. running time is higher than Co-teaching. The Scale-teaching paradigm uses multiple encoders for training and has an additional noisy label correction module, which is expected to increase the training time. Nevertheless, the larger the sampling scale (coarse scale) of the training data used by the Scale-teaching paradigm, the lower the training elapsed time of its model. For example, with input scales of [1, 8, 32], the training time of Scale-teaching is lower than that of CULCU and SREA. On the SleepEEG dataset with the largest number of samples, we find that FINE with an encoder has a higher running time because FINE using all training samples to select clean labels is time-consuming when the sample size is large. In contrast, the runtime of Scale-teaching is lower than FINE. Also, when the input scales are set to [1,8,32], the runtime of Scale-teaching is lower than SREA.
It is worth noting that when Scale-teaching is trained using two scales, such as [1,2] or [1,16], its training run time decreases further. From the analysis in Appendix E, it is clear that Scale-teaching using three different scales generally performs better than two scales for classification with noisy labels. In addition, the classification performance of [1,2,4], [1,4,16], and [1,8,32] when Scale-teaching is trained using three different scales has less difference in classification performance on datasets with longer sequences (e.g., FD-A and Sleep-EDF). The above results indicate that the Scale-teaching paradigm has a greater advantage in runtime on time-series datasets with longer sequences.
Robustness analysis. Three time-series supervised classification methods (Boss [15], Rocket [60] and FCN [57]) and the Scale-teaching paradigm are chosen for robustness analysis against time-series noise labels. Boss [15] is a time series classification method based on similarity search, which can effectively mitigate the negative impact of noise (e.g., adding Gaussian noise) in time series values on classification. Rocket [60] uses a large number of randomly initialized convolution kernels to extract time series features, and employs the extracted features to classify time series using a machine learning classifier (e.g., Ridge classifier). FCN [57] is the encoder used by Scale-teaching, which is a time series classification method based on DNNs. As shown in Table 10, the classification performance of the Scale-teaching paradigm using FCN as encoders is better than that of Boss, Rocket and FCN in the presence of noisy labels. It is worth noting that both Boss and Rocket training processes are independent of the optimization of DNNs. However, their classification performance is still reduced due to the influence of noisy labels. In addition, the encoder of the Scale-teaching paradigm can be designed flexibly, such as using ResNet [1], InceptionTime [65] and OS-CNN [20] in the field of time series classification. In other words, using better robustness encoders, the classification performance of Scale-teaching can be further improved with time-series noise labels.
Table 15: The detailed test classification accuracy (%) results on UEA 30 archive with Sym  $20\%$  noisy labels.
ID	Dataset	Standard	MixUp	Co-teaching	FINE	SREA	SELC	CULCU	Scale-teaching
1	ArticulatoryWordRecognition	89.33	81.04	81.77	41.07	90.13	87.87	84.33	92.73
2	AtrialFibrillation	26.67	25.67	26.67	33.33	26.67	26.67	22.67	28.00
3	BasicMotions	97.10	97.40	99.25	91.00	97.50	98.00	97.50	95.50
4	CharacterTrajectories	98.16	98.49	92.59	7.45	96.54	98.93	98.66	98.93
5	Cricket	94.72	94.17	91.53	41.11	97.50	96.67	93.89	97.28
6	DuckDuckGeese	48.40	52.48	48.60	24.40	46.80	50.00	49.60	45.60
7	EigenWorms	53.56	55.42	62.88	34.66	61.80	64.58	64.10	61.25
8	Epilepsy	84.96	80.28	93.33	82.46	88.70	86.38	93.33	87.59
9	EthanolConcentration	24.15	25.48	26.43	24.71	25.11	24.87	24.39	27.42
10	ERing	73.11	73.70	70.04	16.67	64.59	72.59	61.70	72.77
11	FaceDetection	52.90	51.93	52.36	51.96	50.93	52.30	52.69	53.09
12	FingerMovements	50.80	50.44	52.76	52.20	50.20	52.80	52.30	54.68
13	HandMovementDirection	28.92	32.22	35.14	24.05	19.46	28.11	29.16	35.35
14	Handwriting	35.71	28.55	30.71	25.31	16.63	28.19	34.12	41.35
15	Heartbeat	52.00	52.93	57.46	72.10	62.75	50.17	66.39	51.76
16	InsectWingbeat	62.31	53.90	64.74	63.92	51.56	64.63	64.75	63.85
17	JapaneseVowels	88.99	87.61	94.70	49.73	97.41	93.51	97.19	95.62
18	Libras	74.60	70.07	77.00	10.78	73.22	76.78	70.83	79.67
19	LSST	49.68	48.91	50.23	51.61	35.53	50.48	51.91	55.16
20	MotorImagery	51.00	50.88	51.60	50.80	55.92	52.80	50.10	50.84
21	NATOPS	80.78	80.40	90.56	25.78	89.33	82.67	87.17	91.11
22	PenDigits	96.04	97.58	97.33	98.30	97.90	98.27	98.16	98.12
23	PEMS-SF	62.08	62.87	60.06	14.91	64.97	62.08	61.10	63.05
24	PhonemeSpectra	21.89	23.46	22.25	18.00	6.23	22.82	23.74	26.50
25	RacketSports	76.84	73.50	78.64	25.00	75.74	77.24	79.41	76.87
26	SelfRegulationSCP1	76.81	77.49	78.91	56.86	49.83	78.43	77.55	81.28
27	SelfRegulationSCP2	47.47	49.24	48.32	50.89	50.00	47.44	50.66	51.78
28	SpokenArabicDigits	96.17	96.21	98.83	27.79	98.52	98.67	98.71	99.18
29	StandWalkJump	44.00	40.00	36.00	33.33	45.33	42.67	33.40	34.13
30	UWaveGestureLibrary	76.90	76.97	74.61	12.50	74.29	77.75	66.97	78.12
Avg Acc	63.87	62.98	64.84	40.42	62.04	64.81	64.55	66.29
Avg Rank	5.03	5.20	3.83	6.37	4.77	3.73	4.00	2.73
P-value	6.61E-04	3.33E-04	2.69E-02	2.37E-05	1.14E-02	2.63E-02	3.93E-02	-
Table 16: The detailed test classification accuracy (%) results on UEA 30 archive with Sym  $50\%$  noisy labels.
ID	Dataset	Standard	MixUp	Co-teaching	FINE	SREA	SELC	CULCU	Scale-teaching
1	ArticulatoryWordRecognition	66.64	55.57	52.12	7.53	67.33	57.40	49.63	68.68
2	AtrialFibrillation	28.00	26.67	29.33	33.33	29.33	30.67	32.00	36.00
3	BasicMotions	56.00	58.20	70.75	54.50	81.00	59.00	71.25	60.20
4	CharacterTrajectories	90.62	87.56	67.75	6.73	95.28	93.82	97.03	97.23
5	Cricket	67.44	60.00	68.89	8.33	84.28	66.67	68.06	87.27
6	DuckDuckGeese	32.00	36.64	29.60	26.00	34.00	33.20	33.20	33.84
7	EigenWorms	49.62	45.37	55.65	32.98	38.78	56.79	56.09	49.28
8	Epilepsy	57.39	57.25	64.16	36.38	62.62	60.00	63.55	64.36
9	EthanolConcentration	25.20	25.32	26.05	25.02	27.56	25.62	24.58	26.27
10	ERing	44.30	44.39	40.63	16.67	39.35	44.52	38.54	45.10
11	FaceDetection	48.54	49.81	49.30	49.51	48.94	49.11	48.58	50.72
12	FingerMovements	50.40	50.20	51.80	51.40	50.60	50.80	51.30	54.40
13	HandMovementDirection	26.22	25.51	25.41	26.11	23.51	26.57	24.35	27.08
14	Handwriting	19.54	19.53	19.99	13.41	7.53	19.18	18.28	21.06
15	Heartbeat	55.32	53.52	52.20	54.44	55.40	54.63	53.27	48.62
16	InsectWingbeat	49.30	32.34	58.02	52.25	31.07	53.97	58.13	52.01
17	JapaneseVowels	60.14	59.28	73.97	15.03	70.65	66.43	78.11	79.23
18	Libras	47.09	43.98	51.39	6.67	44.78	50.11	40.06	49.78
19	LSST	47.29	44.58	46.21	47.89	34.35	46.33	47.92	48.75
20	MotorImagery	50.84	51.48	50.10	51.40	52.00	50.60	49.70	49.80
21	NATOPS	54.33	52.53	60.17	16.67	59.80	53.44	58.06	59.56
22	PenDigits	93.38	85.29	95.92	92.85	92.74	96.80	96.53	93.89
23	PEMS-SF	41.20	40.55	32.96	14.45	42.43	43.24	37.57	41.27
24	PhonemeSpectra	19.08	19.11	19.94	11.48	3.92	19.69	19.23	20.09
25	RacketSports	52.50	51.03	52.80	24.08	53.29	52.89	56.58	54.21
26	SelfRegulationSCP1	47.41	42.68	48.86	48.60	49.97	48.33	57.93	58.08
27	SelfRegulationSCP2	48.13	48.22	47.61	48.78	50.00	49.22	50.00	48.73
28	SpokenArabicDigits	85.64	69.95	96.55	96.16	99.23	95.66	97.59	97.69
29	StandWalkJump	38.67	37.87	40.67	33.33	42.67	42.67	37.33	44.00
30	UWaveGestureLibrary	50.41	48.60	45.45	12.50	53.91	49.00	37.94	52.61
Avg Acc	50.09	47.43	50.81	33.82	50.88	51.55	51.75	53.99
Avg Rank	5.17	5.73	4.23	6.23	3.93	3.83	4.30	2.43
P-value	2.98E-04	7.40E-05	1.59E-02	9.35E-05	1.67E-02	1.08E-02	3.75E-02	-