Rfknn: Rough-Fuzzy KNN for Big Data Classification

Mahfouz, Mohamed A.

doi:10.26483/ijarcs.v9i2.5667

Cited by 6 publications

(2 citation statements)

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“…Although the KNN classifier is extremely slow and lazy learner [60,61,62,63,64,65], it is still used extensively for Big data classification, because it skips the training process, and save its time, which is extremely long for most of the traditional classifiers. However, it is used with a helper to hasten the test phase: indexing, clustering, sorting, and reducing the data among other reprocesses help speed up the classifier when dealing with Big data.…”

Section: Literature Reviewmentioning

confidence: 99%

Magnetic Force Classifier: A Novel Method for Big Data Classification

et al. 2022

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There are a plethora of invented classifiers in Machine learning literature, however, there is no optimal classifier in terms of accuracy and time taken to build the trained model, especially with the tremendous development and growth of Big data. Hence, there is still room for improvement. In this paper, we propose a new classification method that is based on the well-known magnetic force. Based on the number of points belonging to a specific class/magnet, the proposed magnetic force (MF) classifier calculates the magnetic force at each discrete point in the feature space. Unknown examples are classified using the magnetic forces recorded in the trained model by various magnets/classes. When compared to existing classifiers, the proposed MF classifier achieves comparable classification accuracy, according to the experimental results utilizing 28 different datasets. More importantly, we found that the proposed MF classifier is significantly faster than all other classifiers tested, particularly when applied to Big datasets and hence could be a viable option for structured Big data classification with some optimization.

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Section: Literature Reviewmentioning

confidence: 99%

Magnetic Force Classifier: A Novel Method for Big Data Classification

et al. 2022

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“…Chen et al [20] used the FLANN library, which contains fast approximate KNN searches in high-dimensional spaces, for the purpose of large-scale data fast density peak clustering. In [21], rough and fuzzy sets concepts are used to distinguish between core and border objects. The author partitions data into several clusters, and then, for each unseen data sample, the nearest neighbors would be searched in one core and some border partitions according to its membership in the clusters.…”

Section: Related Workmentioning

confidence: 99%

A New K-Nearest Neighbors Classifier for Big Data Based on Efficient Data Pruning

et al. 2020

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The K-nearest neighbors (KNN) machine learning algorithm is a well-known non-parametric classification method. However, like other traditional data mining methods, applying it on big data comes with computational challenges. Indeed, KNN determines the class of a new sample based on the class of its nearest neighbors; however, identifying the neighbors in a large amount of data imposes a large computational cost so that it is no longer applicable by a single computing machine. One of the proposed techniques to make classification methods applicable on large datasets is pruning. LC-KNN is an improved KNN method which first clusters the data into some smaller partitions using the K-means clustering method; and then applies the KNN for each new sample on the partition which its center is the nearest one. However, because the clusters have different shapes and densities, selection of the appropriate cluster is a challenge. In this paper, an approach has been proposed to improve the pruning phase of the LC-KNN method by taking into account these factors. The proposed approach helps to choose a more appropriate cluster of data for looking for the neighbors, thus, increasing the classification accuracy. The performance of the proposed approach is evaluated on different real datasets. The experimental results show the effectiveness of the proposed approach and its higher classification accuracy and lower time cost in comparison to other recent relevant methods.

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