Table 4. Mean classification accuracies (%) of OSWLDA, OPCALDA, and OLDA evaluated on data set A using cross-validation.

Algorithms			Intensification sequences
			1	2	3	4	5	6	7	8	9	10	11	12	13	14	15
OSWLDA	5	1	31.2	48.0	60.0	68.4	72.8	75.4	80.8	83.6	86.0	87.4	89.8	91.4	90.0	90.6	91.8
		2	37.6	54.8	68.0	74.0	81.4	82.4	87.6	89.8	91.2	92.4	93.8	94.2	94.4	95.2	95.0
		3	36.4	57.4	69.4	77.2	82.4	85.0	87.8	90.0	91.4	92.6	94.6	94.6	94.6	95.0	94.8
		4	37.2	55.4	71.4	77.6	84.0	85.4	87.6	90.2	92.2	93.6	93.8	94.6	95.0	95.2	95.6
		5	35.6	53.6	66.8	74.8	79.2	83.4	85.6	89.4	91.6	93.0	93.0	94.4	94.0	94.2	95.2
OPCALDA	5	1	29.4	44.6	57.6	63.6	71.8	74.6	76.8	80.6	82.6	84.8	87.6	88.0	88.6	88.4	90.6
		2	36.6	53.4	66.4	75.0	81.2	84.2	85.6	89.6	90.8	91.0	92.6	92.4	93.8	94.2	95.0
		3	38.2	54.4	67.6	75.2	81.6	84.2	87.4	90.4	92.0	91.8	93.2	93.6	94.4	95.6	96.0
		4	38.6	55.8	67.8	74.4	82.2	85.8	88.0	91.4	93.0	92.4	94.0	93.6	95.0	95.6	96.0
		5	37.8	55.4	68.6	74.8	82.2	85.2	87.4	90.4	92.0	92.4	93.8	94.4	94.8	95.8	95.8
OLDA	5	1	2.6	4.0	3.4	3.8	4.0	4.2	3.2	4.0	4.4	4.6	4.6	5.2	4.6	5.0	5.4
		2	25.6	37.0	49.8	56.8	65.2	69.6	75.2	79.8	80.0	82.6	83.6	83.6	85.2	85.4	88.0
		3	28.2	43.2	57.8	65.8	72.8	76.4	79.4	82.4	86.2	88.0	89.0	89.4	91.0	91.8	92.8
		4	29.2	44.8	59.8	67.8	73.2	78.8	80.8	84.2	86.8	89.6	91.0	90.6	91.8	93.2	94.4
		5	28.2	44.4	59.8	67.6	73.2	78.8	80.4	83.4	85.4	89.6	91.0	90.4	91.6	93.2	94.2

The best accuracy among all for each algorithm and each repetition is written in bold and the worst is underlined. An overlapped ensemble classifier becomes an ensemble classifier with naive partitioning when and . The classifier is equivalent to a single classifier when and .