Hi,
I would like to test and compare the nearest neighbor implementations of WEKA and KNIME. To do this I have used the following datasets:
- abalone.data.test
- abalone.data.train
(you can find them in the datasets section: http://www.knime.org/files/datasets.zip)
To do this I have built a couple of workflows, one with the Nearest Neighbor (KNIME) node, and the other with the IB1 (WEKA). Following, the configurations for each one:
- Nearest Neighbor (KNIME)
Number of neighbors to consider: 1 (K1)
Weight neighbors by distance: disabled
Results: Accuracy: 48.94%
2. IB1 (WEKA)
This node only considers 1 neighbor in the classification process (K1)
Results: Accuracy: 48.18%
I can not see the the cause of that difference. What could be the reason of these different results, using a so straightforward algorithm as K1 ?
thanks in advance
Oscar