Fast neighbor search by using revised k-d tree

Chen, Yewang; Zhou, Lida; Tang, Yi; Singh, Jai Puneet; Bouguila, Nizar; Wang, Cheng; Wang, Huazhen; Du, Ji-Xiang

doi:10.1016/j.ins.2018.09.012

Cited by 80 publications

(28 citation statements)

References 38 publications

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“…Similarly, the performance of the KNN algorithm was improved based on the revised buffer kd-tree integration. A fast neighbor search through the revised kd-tree was realized although the method is not suitable for high dimensional data [24]. With respect to high dimensional data problems, the scalable nearest neighbor method through the introduction of the k-means tree for fast approximate matching of binary features along with the k-d forest was proposed and found to be effective in addressing the issues arising when scaling to very large size data sets [25].…”

Section: Literature Reviewmentioning

confidence: 99%

An Enhanced K-Nearest Neighbor Predictive Model through Metaheuristic Optimization

Delima¹

2020

IJACSA

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The K-Nearest Neighbor (KNN) algorithm is vulnerable to noise, which is rooted in the dataset and has negative effects on its accuracy. Hence, various researchers employed variable minimization techniques before predicting the KNN in the quest so as to improve its predictive capability. The Genetic Algorithm (GA) is the most widely used metaheuristics for such purpose; however, the GA suffers a problem, which is its mating scheme bounded on its crossover operator. Thus, the use of the novel Inversed Bi-segmented Average Crossover (IBAX) was observed. In the present work, the crossover improved genetic algorithm (CIGAL) was instrumental in the enhancement of KNN's prediction accuracy. The use of the unmodified genetic algorithm had removed 13 variables; while the CIGAL then further removed 20 variables from the 30 total variables in the faculty evaluation dataset. Consequently, the integration of the CIGAL to the KNN (CIGAL-KNN) prediction model improved the KNN prediction accuracy to 95.53%. In contrast to the model of having the unmodified genetic algorithm (GA-KNN); the use of the lone KNN algorithm, the prediction accuracy is only at 89.94% and 87.15%, respectively. To validate the accuracy of the models, the use of the 10-folds cross-validation technique revealed a 93.13%, 89.27%, and 87.77% prediction accuracy of the CIGAL-KNN, GA-KNN, and KNN prediction models, respectively. The above results show that the CIGAL carried out an optimized GA performance and increased the accuracy of the KNN algorithm as a prediction model.

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Section: Literature Reviewmentioning

confidence: 99%

An Enhanced K-Nearest Neighbor Predictive Model through Metaheuristic Optimization

Delima¹

2020

IJACSA

View full text Add to dashboard Cite

show abstract

“…A fast neighbor search through the revised kd-tree integration was realized . however, the method is not suitable for high dimensional data [24]. With respect to high dimensional data problems, the scalable nearest neighbor method through the the k-means tree introduction for fast approximate matching of binary features along with the k-d forest was proposed and found to be effective in addressing that the issues arise when scaling to very large size data sets [25].…”

Section: Related Literaturementioning

confidence: 99%

An Enhanced K-Nearest Neighbor Predictive Model through Metaheuristic Optimization

Delima

2020

Int. j. eng. technol. innov.

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The k-nearest neighbor (KNN) algorithm is vulnerable to noise, which is rooted in the dataset and has negative effects on its accuracy. Hence, various researchers employ variable minimization techniques before predicting the KNN in the quest so as to improve its predictive capability. The genetic algorithm (GA) is the most widely used metaheuristics for such purpose; however, the GA suffers a problem that its mating scheme is bounded on its crossover operator. Thus, the use of the novel inversed bi-segmented average crossover (IBAX) is observed. In the present work, the crossover improved genetic algorithm (CIGAL) is instrumental in the enhancement of KNN’s prediction accuracy. The use of the unmodified genetic algorithm has removed 13 variables, while the CIGAL then further removes 20 variables from the 30 total variables in the faculty evaluation dataset. Consequently, the integration of the CIGAL to the KNN (CIGAL-KNN) prediction model improves the KNN prediction accuracy to 95.53%. In contrast to the model of having the unmodified genetic algorithm (GA-KNN), the use of the lone KNN algorithmand the prediction accuracy is only at 89.94% and 87.15%, respectively. To validate the accuracy of the models, the use of the 10-folds cross-validation technique reveals 93.13%, 89.27%, and 87.77% prediction accuracy of the CIGAL-KNN, GA-KNN, and KNN prediction models, respectively. As the result, the CIGAL carried out an optimized GA performance and increased the accuracy of the KNN algorithm as a prediction model.

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“…Regions in multi-resolution aggregation need to be grouped and linked for quick reference. Many variations of tree structures have been used to save data for fast access such as CF tree used in BIRCH [31], R* tree used in DBSCAN [32,33], KD-tree [34], aR-tree [35], and R+-tree [36]. However, these trees do not meet all of our requirements such as hosting aggregated data, query independence, simple creation, and incremental updates.…”

Section: Techniques For Data Reductionmentioning

confidence: 99%

Bayes Classification and Entropy Discretization of Large Datasets using Multi-Resolution Data Aggregation

Alwajidi¹,

Yang²

2020

Adv. sci. technol. eng. syst. j.

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Big data analysis has important applications in many areas such as sensor networks and connected healthcare. High volume and velocity of big data bring many challenges to data analysis. One possible solution is to summarize the data and provides a manageable data structure to hold a scalable summarization of data for efficient and effective analysis. This research extends our previous work on developing an effective technique to create, organize, access, and maintain summarization of big data and develops algorithms for Bayes classification and entropy discretization of large data sets using the multi-resolution data summarization structure. Bayes classification and data discretization play essential roles in many learning algorithms such as decision tree and nearest neighbor search. The proposed method can handle streaming data efficiently and, for entropy discretization, provide sufficient information to find the optimal split variable and the optimal split value.

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Fast neighbor search by using revised k-d tree

Cited by 80 publications

References 38 publications

An Enhanced K-Nearest Neighbor Predictive Model through Metaheuristic Optimization

An Enhanced K-Nearest Neighbor Predictive Model through Metaheuristic Optimization

An Enhanced K-Nearest Neighbor Predictive Model through Metaheuristic Optimization

Bayes Classification and Entropy Discretization of Large Datasets using Multi-Resolution Data Aggregation

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