An Improved k-Nearest Neighbor Classification Using Genetic Algorithm
k-Nearest Neighbor (KNN) is one of the most popular
algorithms for pattern recognition. Many researchers have
found that the KNN algorithm accomplishes very good
performance in their experiments on different data sets. The
traditional KNN text classification algorithm has three
limitations: (i) calculation complexity due to the usage of all
the training samples for classification, (ii) the performance is
solely dependent on the training set, and (iii) there is no
weight difference between samples. To overcome these
limitations, an improved version of KNN is proposed in this
paper. Genetic Algorithm (GA) is combined with KNN to
improve its classification performance. Instead of
considering all the training samples and taking k-neighbors,
the GA is employed to take k-neighbors straightaway and
then calculate the distance to classify the test samples.
Before classification, initially the reduced feature set is
received from a novel method based on Rough set theory
hybrid with Bee Colony Optimization (BCO) as we have
discussed in our earlier work. The performance is compared
with the traditional KNN, CART and SVM classifiers.
Keywords: k-Nearest Neighbor, Genetic Algorithm, Support
Vector Machine, Rough Set
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