A Novel Scheme for Improving Accuracy of KNN Classification Algorithm Based on the New Weighting Technique and Stepwise Feature Selection
K nearest neighbor algorithm is one of the most frequently used techniques in data mining
for its integrity and performance. Though the KNN algorithm is highly effective in many …
for its integrity and performance. Though the KNN algorithm is highly effective in many …
[PDF][PDF] Mime-knn: improve knn classifier performance include classification accuracy and time consumption
T Shang, X Xia, J Zheng - DEStech Transactions on Computer …, 2018 - scholar.archive.org
ABSTRACT The K Nearest Neighbor (KNN) classifier has been widely used in the
applications of data mining and machine learning, because of its simple implementation and …
applications of data mining and machine learning, because of its simple implementation and …
Combining feature selection with feature weighting for k-NN classifier
Y Bao, X Du, N Ishii - … Conference on Intelligent Data Engineering and …, 2002 - Springer
The k-nearest neighbor (k-NN) classification is a simple and effective classification
approach. However, it suffers from over-sensitivity problem due to irrelevant and noisy …
approach. However, it suffers from over-sensitivity problem due to irrelevant and noisy …
An improved KNN algorithm based on ensemble methods and correlation
K-Nearest Neighbors is a widely used algorithm due to its simplicity and efficacity. However,
KNN suffers from many drawbacks, such as it does not work well with datasets with a high …
KNN suffers from many drawbacks, such as it does not work well with datasets with a high …
[PDF][PDF] Predicting the number of nearest neighbor for kNN classifier
Y Li, Y Yang, J Che, L Zhang - IAENG International Journal of Computer …, 2019 - iaeng.org
The k nearest neighbor (kNN) rule is known as its simplicity, effectiveness, intuitiveness and
competitive classification performance. Selecting the parameter k with the highest …
competitive classification performance. Selecting the parameter k with the highest …
[PDF][PDF] An enhancement of k-nearest neighbor classification using genetic algorithm
AKNSM Rahman, A Salah - 2005 - micsymposium.org
Abstract K-Nearest Neighbor Classification (kNNC) makes the classification by getting votes
of the k-Nearest Neighbors. Performance of kNNC is depended largely upon the efficient …
of the k-Nearest Neighbors. Performance of kNNC is depended largely upon the efficient …
Improving performance of the k-nearest neighbor classifier by combining feature selection with feature weighting
Y Bao, X Du, N Ishii - Transactions of the Japanese Society for …, 2002 - jstage.jst.go.jp
The k-nearest neighbor (k-NN) classification is a simple and effective classification
approach. However, it suffers from over-sensitivity problem due to irrelevant and noisy …
approach. However, it suffers from over-sensitivity problem due to irrelevant and noisy …
[PDF][PDF] An improved K-nearest neighbor algorithm using tree structure and pruning technology
J Li - Intelligent automation and soft computing, 2019 - cdn.techscience.cn
ABSTRACT K-Nearest Neighbor algorithm (KNN) is a simple and mature classification
method. However there are susceptible factors influencing the classification performance …
method. However there are susceptible factors influencing the classification performance …
An enhanced k-nearest neighbor algorithm using information gain and clustering
KNN (k-nearest neighbor) is an extensively used classification algorithm owing to its
simplicity, ease of implementation and effectiveness. It is one of the top ten data mining …
simplicity, ease of implementation and effectiveness. It is one of the top ten data mining …
A feature weighted K-nearest neighbor algorithm based on association rules
K-nearest neighbors (kNN) is a popular machine learning algorithm because of its clarity,
simplicity, and efficacy. kNN has numerous drawbacks, including ignoring issues like class …
simplicity, and efficacy. kNN has numerous drawbacks, including ignoring issues like class …