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Table 2: The accuracy of each algorithm in each dataset.
Algorithm KNN WKNN DWKNN LMKNN LMPNN LDBNN Average
Audiology 0.73 0.73 0.76 0.73 0.73 0.80 0.75
Breast cancer 0.73 0.73 0.74 0.66 0.71 0.76 0.72
Cars 0.70 0.70 0.95 0.93 0.93 0.95 0.86
Diabetes 0.69 0.69 0.71 0.73 0.72 0.75 0.71
E. Coli 0.82 0.82 0.83 0.87 0.86 0.87 0.84
Glass 0.73 0.73 0.75 0.75 0.76 0.76 0.75
Ionosphere 0.91 0.91 0.91 0.92 0.91 0.91 0.91
Iris 0.95 0.95 0.95 0.97 0.96 0.96 0.96
Letter 0.96 0.96 0.97 0.97 0.97 0.97 0.97
Mushroom 1.00 1.00 1.00 1.00 1.00 1.00 1.00
Promoters 0.78 0.78 0.79 0.80 0.86 0.89 0.82
Segment 0.97 0.97 0.97 0.97 0.98 0.97 0.97
Soybean 0.92 0.92 0.92 0.91 0.93 0.93 0.92
Splice 0.79 0.79 0.80 0.75 0.84 0.87 0.81
Voting 0.92 0.92 0.93 0.93 0.94 0.94 0.93
Zoo 0.96 0.96 0.96 0.98 0.98 0.98 0.97
Average 0.85 0.85 0.87 0.87 0.88 0.89 0.82
instances, are sparsely distributed, in general.
The experiments show that LDBNN achieves the
highest accuracy in most of the considered datasets
and the highest average accuracy. Also, the LDBNN
algorithm is the only algorithm whose accuracy is
higher than the average in all datasets. Although, in
some datasets, the LDBNN achieves accuracy rates
that are similar to those achieved by other algorithms
(such as LMPNN), in some datasets it provides much
higher accuracy rates. This suggests that the concepts
underlying the LDBNN algorithm are powerful no-
tions that should be investigated in the future for de-
veloping better classification algorithms.
In the future, we plan to investigate how to com-
bine the LDBNN algorithm with other algorithms
that achieve a significant performance, such as the
LMPNN algorithm.
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