Table 2.

For each ADE dataset, the number of features included in the learning process with different sparsity requirements

	0.2	0.3	0.5	0.7	0.9	0.95	1.0
D61.1	16	21	23	34	72	90	186
E27.3	11	12	14	19	42	88	137
G62.0	4	11	16	19	40	62	151
I95.2	11	13	14	20	30	56	180
L27.0	4	12	18	25	33	54	162
L27.1	6	11	17	24	35	62	169
M80.4	9	11	14	19	42	62	170
O35.5	1	2	4	15	24	38	73
T78.2	8	9	12	17	29	50	168
T78.3	8	9	12	17	27	43	131
T78.4	8	9	13	17	29	51	194
T80.1	11	13	19	25	33	40	131
T80.8	11	14	19	25	33	43	128
T88.6	11	12	15	21	33	59	202
T88.7	11	12	16	21	33	62	217

In particular, each column corresponds to the maximum percentage of empty time series which is tolerated for a dataset. When τ_sp=1.0, all the available features are taken into account, regardless of the percentage of empty sequences; the only requirement for a feature to be selected in the latter case is that it contains at least one non-empty sequence