International Scholarly Research Notices

Research Article

A Novel Neuron in Kernel Domain

Table 4

Evaluation of online learning algorithms on the small and medium datasets.


Algorithm	Sonar				Ionosphere
Algorithm	Density (%)	Training time (s)	Training mistake (%)	Test mistake (%)	Density (%)	Training time (s)	Training mistake (%)	Test mistake (%)

Perceptron	35.722	0.031	35.722	39.952	15.886	0.038	20.675	48.117
ROMMA	69.946	0.066	33.048	39.405	68.829	0.084	22.829	42.260
ALMA	42.139	0.042	33.961	34.643	45.949	0.057	23.601	46.907
PA	77.112	0.069	33.636	37.571	67.785	0.094	18.714	45.470
DOUL	71.551	0.078	31.818	39.428	68.449	0.114	24.727	48.126
KNN	98.716	0.056	27.166	26.857	99.968	0.139	27.331	39.395
AKNN	100	0.059	28.343	25.357	95.032	0.132	32.894	39.361
AKNN_μ	20.267	0.038	25.561	24.024	38.418	0.077	45.338	38.269

Algorithm	Pima				German
Algorithm	Density (%)	Training time (s)	Training mistake (%)	Test mistake (%)	Density (%)	Training time (s)	Training mistake (%)	Test mistake (%)

Perceptron	30.679	0.313	30.679	45.955	40.289	0.637	40.289	35.0
ROMMA	51.185	0.527	30.824	45.957	99.922	1.574	34.555	33.7
ALMA	32.442	0.345	29.003	43.481	99.722	1.574	34.600	34.4
PA	61.257	0.609	29.176	44.400	99.911	1.578	34.544	33.3
DOUL	55.390	0.635	31.228	49.086	99.911	2.539	34.544	31.4
KNN	97.471	0.489	25.462	23.689	100	0.824	45.022	39.6
AKNN	94.957	0.494	25.144	24.735	100	0.876	31.733	29.7
AKNN_μ	26.084	0.237	28.367	25.916	3.422	0.169	30.189	29.9

Algorithm	Splice				Cloud
Algorithm	Density (%)	Training time (s)	Training mistake (%)	Test mistake (%)	Density (%)	Training time (s)	Training mistake (%)	Test mistake (%)

Perceptron	46.033	0.715	46.033	49.3	0.222	0.048	0.222	25.243
ROMMA	99.611	1.538	43.389	46.4	14.248	1.191	0.108	25.441
ALMA	95.933	1.479	44.022	43.9	0.494	0.101	0.125	24.661
PA	98.455	1.507	43.378	45.5	3.076	0.332	0.108	25.292
DOUL	98.455	2.305	43.378	45.6	3.076	0.339	0.108	23.536
KNN	36.000	0.176	13.967	42.4	44.927	0.697	0.054	0
AKNN	92.000	0.765	39.289	41.7	44.927	0.737	0.108	0
AKNN_μ	81.188	0.694	40.155	41.5	0.488	0.144	0.103	0

Bold number shows the algorithm with the best efficiency measurement in each dataset.