scholarly journals A preliminary study on Hybrid Spill-Tree Fuzzy k-Nearest Neighbors for big data classification

2018 ◽  
Author(s):  
Jesus Maillo ◽  
Julian Luengo ◽  
Salvador Garcia ◽  
Francisco Herrera ◽  
Isaac Triguero
Keyword(s):  
Big Data ◽  
Data Classification ◽  
Nearest Neighbors ◽  
K Nearest Neighbors ◽  
Preliminary Study ◽  
Big Data Classification

scholarly journals Furthest-Pair-Based Decision Trees: Experimental Results on Big Data Classification

Information ◽  
2018 ◽  
Vol 9 (11) ◽  
pp. 284 ◽  
Author(s):  
Ahmad Hassanat
Keyword(s):  
Big Data ◽  
Decision Trees ◽  
Computation Time ◽  
Data Classification ◽  
Search Tree ◽  
Experimental Results ◽  
Binary Search Tree ◽  
K Nearest Neighbors ◽  
Time Space ◽  
Big Data Classification

Big Data classification has recently received a great deal of attention due to the main properties of Big Data, which are volume, variety, and velocity. The furthest-pair-based binary search tree (FPBST) shows a great potential for Big Data classification. This work attempts to improve the performance the FPBST in terms of computation time, space consumed and accuracy. The major enhancement of the FPBST includes converting the resultant BST to a decision tree, in order to remove the need for the slow K-nearest neighbors (KNN), and to obtain a smaller tree, which is useful for memory usage, speeding both training and testing phases and increasing the classification accuracy. The proposed decision trees are based on calculating the probabilities of each class at each node using various methods; these probabilities are then used by the testing phase to classify an unseen example. The experimental results on some (small, intermediate and big) machine learning datasets show the efficiency of the proposed methods, in terms of space, speed and accuracy compared to the FPBST, which shows a great potential for further enhancements of the proposed methods to be used in practice.

scholarly journals Norm-Based Binary Search Trees for Speeding Up KNN Big Data Classification

Computers ◽  
2018 ◽  
Vol 7 (4) ◽  
pp. 54 ◽  
Author(s):  
Ahmad Hassanat
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Data Classification ◽  
Search Tree ◽  
Binary Search ◽  
Binary Search Tree ◽  
K Nearest Neighbors ◽  
Knn Classifier ◽  
Big Data Classification ◽  
Training Examples

Due to their large sizes and/or dimensions, the classification of Big Data is a challenging task using traditional machine learning, particularly if it is carried out using the well-known K-nearest neighbors classifier (KNN) classifier, which is a slow and lazy classifier by its nature. In this paper, we propose a new approach to Big Data classification using the KNN classifier, which is based on inserting the training examples into a binary search tree to be used later for speeding up the searching process for test examples. For this purpose, we used two methods to sort the training examples. The first calculates the minimum/maximum scaled norm and rounds it to 0 or 1 for each example. Examples with 0-norms are sorted in the left-child of a node, and those with 1-norms are sorted in the right child of the same node; this process continues recursively until we obtain one example or a small number of examples with the same norm in a leaf node. The second proposed method inserts each example into the binary search tree based on its similarity to the examples of the minimum and maximum Euclidean norms. The experimental results of classifying several machine learning big datasets show that both methods are much faster than most of the state-of-the-art methods compared, with competing accuracy rates obtained by the second method, which shows great potential for further enhancements of both methods to be used in practice.

Research on complex attribute big data classification based on iterative fuzzy clustering algorithm

2021 ◽  
pp. 1-12
Author(s):  
Li Qian
Keyword(s):  
Big Data ◽  
Fuzzy Clustering ◽  
Classification Accuracy ◽  
Clustering Algorithm ◽  
Principal Component ◽  
Data Classification ◽  
Fisher Discriminant Analysis ◽  
Fuzzy Clustering Algorithm ◽  
Local Fisher Discriminant Analysis ◽  
Big Data Classification

In order to overcome the low classification accuracy of traditional methods, this paper proposes a new classification method of complex attribute big data based on iterative fuzzy clustering algorithm. Firstly, principal component analysis and kernel local Fisher discriminant analysis were used to reduce dimensionality of complex attribute big data. Then, the Bloom Filter data structure is introduced to eliminate the redundancy of the complex attribute big data after dimensionality reduction. Secondly, the redundant complex attribute big data is classified in parallel by iterative fuzzy clustering algorithm, so as to complete the complex attribute big data classification. Finally, the simulation results show that the accuracy, the normalized mutual information index and the Richter’s index of the proposed method are close to 1, the classification accuracy is high, and the RDV value is low, which indicates that the proposed method has high classification effectiveness and fast convergence speed.

scholarly journals A Reformed K-Nearest Neighbors Algorithm for Big Data Sets

2018 ◽  
Vol 14 (9) ◽  
pp. 1213-1225 ◽  
Author(s):  
Vo Ngoc Phu ◽  
Vo Thi Ngoc Tran
Keyword(s):  
Big Data ◽  
Nearest Neighbors ◽  
Data Sets ◽  
K Nearest Neighbors
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