Knnba : K-Nearest-Neighbor-Based-Association Algorithm

Celal Bayar Üniversitesi Fen Bilimleri Dergisi, 2019

One of the most important problems in machine learning, which has gained importance in recent years, is classification. The k-nearest neighbors (kNN) algorithm is widely used in classification problem because it is a simple and effective method. However, there are several factors affecting the performance of kNN algorithm. One of them is determining an appropriate proximity (distance or similarity) measure. Although the Euclidean distance is often used as a proximity measure in the application of the kNN, studies show that the use of different proximity measures can improve the performance of the kNN. In this study, we propose the Weighted Similarity k-Nearest Neighbors algorithm (WS-kNN) which use a weighted similarity as proximity measure in the kNN algorithm. Firstly, it calculates the weight of each attribute and similarity between the instances in the dataset. And then, it weights similarities by attribute weights and creates a weighted similarity matrix to use as proximity mea...

ArXiv, 2020

kNN is a very effective Instance based learning method, and it is easy to implement. Due to heterogeneous nature of data, noises from different possible sources are also widespread in nature especially in case of large-scale databases. For noise elimination and effect of pseudo neighbours, in this paper, we propose a new learning algorithm which performs the task of anomaly detection and removal of pseudo neighbours from the dataset so as to provide comparative better results. This algorithm also tries to minimize effect of those neighbours which are distant. A concept of certainty measure is also introduced for experimental results. The advantage of using concept of mutual neighbours and distance-weighted voting is that, dataset will be refined after removal of anomaly and weightage concept compels to take into account more consideration of those neighbours, which are closer. Consequently, finally the performance of proposed algorithm is calculated.

ijfancs, 2023

In the world of machine learning, the k-Nearest Neighbors (kNN) algorithm has emerged as a powerful tool for classification tasks. With its simplicity and effectiveness, kNN has gained popularity across various domains. In this article, we will explore the fundamentals of kNN, its applications, and implementation. Whether you are a beginner or an experienced practitioner in machine learning, this article will provide valuable insights into the capabilities of kNN and how it can be leveraged to make accurate predictions. So, let's dive into the world of k-Nearest Neighbors and unlock its potential!

2003

The k-Nearest-Neighbours (kNN) is a simple but effective method for classification. The major drawbacks with respect to kNN are (1) its low efficiency – being a lazy learning method prohibits it in many applications such as dynamic web mining for a large repository, and (2) its dependency on the selection of a “good value” for k. In this paper, we propose a novel kNN type method for classification that is aimed at overcoming these shortcomings. Our method constructs a kNN model for the data, which replaces the data to serve as the basis of classification. The value of k is automatically determined, is varied for different data, and is optimal in terms of classification accuracy. The construction of the model reduces the dependency on k and makes classification faster. Experiments were carried out on some public datasets collected from the UCI machine learning repository in order to test our method. The experimental results show that the kNN based model compares well with C5.0 and kNN in terms of classification accuracy, but is more efficient than the standard kNN.

Proceedings of the 17th International Conference on Pattern Recognition, 2004. ICPR 2004., 2004

The nearest neighbour (NN) classification rule is usually chosen in a large number of pattern recognition systems due to its simplicity and good behaviour. As the problem of finding the nearest neighbour of an unknown sample is also of interest in other scientific communities (very large databases, data mining, computational geometry, . . .), a vast number of fast nearest neighbour search algorithms have been developed during the last years. In order to improve classification rates, the k-NN rule is often used instead of the NN rule, but it yields higher classification times. In this work we introduce a new classification rule applicable to many of those algorithms in order to obtain classification rates better than those of the nearest neighbour (similar to those of the k-NN rule) without significantly increasing classification time.

International Journal of Computer Applications, 2011

The k-nearest neighbor rule is one of the simplest and most attractive pattern classification algorithms. However, it faces serious challenges when patterns of different classes overlap in some regions in the feature space. In the past, many researchers have developed various methods to improve its performance. In this paper, we propose an improved evidence theoretic kNN algorithm which combines Dempster Shafer theory of evidence and k nearest neighbouring rule with distance metric based neighborhood. It is shown that the proposed algorithm significantly improves the performance of the k-nearest neighbor rule. In experiments this algorithm performed better than voting, distance weighted and extended k nearest neighbours algorithms with best k, and it achieved highest performance when number of neighbours considered is seven.

2010 Ninth International Conference on Grid and Cloud Computing, 2010

kNN is a simple, but effective and powerful lazy learning algorithm. It has been now widely used in practice and plays an important role in pattern classification. However, how to choose an optimal value of k is still a challenge, which straightforwardly affects the performance of kNN. To alleviate this problem, in this paper we propose a new learning algorithm under the framework of kNN. The primary characteristic of our method is that it adopts mutual nearest neighbors, rather than k nearest neighbors, to determine the class labels of unknown instances. The advantage of mutual neighbors is that pseudo nearest neighbors can be identified and will not be taken into account during the prediction process. As a result, the final result is more reasonable. Experimental results conducted on UCI datasets show that the classification performance achieved by our proposed method is better than the traditional one.

2009

presented which uses clustering techniques to augment the performance of K-Nearest Neighbor algorithm. This new method is called Nearest Cluster approach, NC. In this algorithm the neighbor samples are automatically determined using clustering techniques. After partitioning the train set, the labels of cluster centers are determined. For specifying the class label of a new test sample, the class label of the nearest cluster prototype is used. Computationally, the NC method is faster than KNN, K 2 times. Also the clustering techniques lead to find the best number of neighbors based on the nature of feature space. The proposed method is evaluated on two standard data sets, SAHeart and Monk. Experimental results show the excellent improvement both in accuracy and time complexity in comparison with the KNN method.

A new C× K nearest neighbor algorithm with a new point of view is proposed. In this algorithm K neighbors from each of the classes are taken into account instead of the well-known K neighbor algorithm in which only the total number of neighbors are considered. After experiments with well-known classification datasets, we conclude that K -NN, weighted K -NN, and average linkage neighbors results are between the single-linkage and complete-linkage algorithms. After the evaluation of the average accuracy results, we realized that the best results are obtained for the values of K between 1 and 10. On the other hand, it is determined that using single linkage strategy provides high values of the results at most of the times.

International Journal of Machine Learning and Cybernetics, 2013

The k-nearest neighbor rule is a simple and effective classifier for document classification. In this method, a document is put into a particular class if the class has the maximum representation among the k nearest neighbors of the documents in the training set. The k nearest neighbors of a test document are ordered based on their content similarity with the documents in the training set. Document classification is very challenging due to the large number of attributes present in the data set. Many attributes, due to the sparsity of the data, do not provide any information about a particular document. Thus, assigning a document to a predefined class for a large value of k may not be accurate when the margin of majority voting is one or when a tie occurs. This article tweaks the knn rule by putting a threshold on the majority voting and the method proposes a discrimination criterion to prune the actual search space of the test document. The proposed classification rule will enhance the confidence of the voting process and it makes no prior assumption about the number of nearest neighbors. The experimental evaluation using various well known text data sets show that the accuracy of the proposed method is significantly better than the traditional knn method as well as some other document classification methods.