k-nearest neighbors algorithm
Rate Movie App: Implementation of K-Nearest Neighbors Algorithm in the Development of Decision Support System for Philippine Movie Rating and Classification
... experiment, K-Nearest Neighbors worked well with the Philippine movie ...dataset. K-Nearest Neighbors outperformed ...Bayes algorithm. This showed as well that the ...
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Title: WHO ARE the LIKELIEST SURVIVORS of BREAST CANCER- An ANALYSIS by K NEAREST NEIGHBORS ALGORITHM
... Abstract— As deaths caused by breast cancer have become a major concern, researchers are trying to design & implement algorithms so that preventive measures can be adopted in an effective way. Hence, the aim of this ...
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Simulation of Agricultural Logistic Processes with k-Nearest Neighbors Algorithm
... the k-Nearest Neighbors algorithm (kNN) is ...data,the algorithm is much more easily adaptable on new ...The k-Nearest Neighbors algorithm which has been ...
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A Completion on Fruit Recognition System Using K Nearest Neighbors Algorithm
... After training data system, ready to use.KNN find out the shortest distance between the feature values of test fruit Images with feature value training fruit images. While KNN algorithm finds out the closest ...
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On Secured Blockchain Technology For K Nearest Neighbors Algorithm
... the utilization of some other magnificence of increased Petri nets. An adjustment of the communicate spreading calculation to include an replacing module has established an wonderful improvement in records scattering ...
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ATM Security using Machine Learning
... --------------------------------------------------------------------------***---------------------------------------------------------------------------- Abstract - Due to use of outdated technology and lack of basic ...
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Effect of roll compaction on granule size distribution of microcrystalline cellulose–mannitol mixtures: computational intelligence modeling and parametric analysis
... and k-nearest neighbors algorithm assisted by sevenfold cross-validation were used to present generalized mod- els for the prediction of GSD based on roll compaction process setting and ...
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Breast Cancer Diagnosis by using k Nearest Neighbor with Different Distances and Classification Rules
... In this paper, we study and analyze several distances and differ- ent values of the nearest neighbors parameter k, by using dif- ferent classification rules in the k-nearest ...
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The Importance of Recommender and Feedback Features in a Pronunciation Learning Aid
... Some of the methods were as simple as reading or counting the data such as counting word frequency whilst others used more advanced machine learn- ing algorithms. In the RS, the New Common Word feature combined the word ...
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Detection Model for Daniel of Service Attacks using Random Forest and k Nearest Neighbors
... Park et al. [22] proposed Correlation-Based Hybrid Feature Selection (CBHFS) approach. GA was used to generate subsets of features from given feature set. CBHFS took full feature set as input and returned the optimal ...
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Comparative Study for Text Document Classification Using Different Machine Learning Algorithms
... Classification is a supervised learning method: the goal is finding the labels of the unknown object. In the real world, the tedious amounts of manual works are required to label the unknown documents. The system is ...
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The Performance of K Nearest Neighbors on Malignant and Benign Classes: Sensitivity, Specificity, and Accuracy Analysis for Breast Cancer Diagnosis
... This algorithm highly depends on the definition of similarity[3]. The distance between two pieces of data shows how much they are different from each other and so the inverse distance represents the similarity ...
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SPATIAL OUTLIER DETECTION USING IMPROVED Z-SCORE TEST
... an algorithm for outlier detection in spatial databases, which depends upon the K-nearest neighbors of each spatial point of the data ...this algorithm the overhead of calculation for ...
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Interpreting Neural Networks with Nearest Neighbors
... Local model interpretation methods explain individual predictions by assigning an impor- tance value to each input feature. This value is often determined by measuring the change in confidence when a feature is removed. ...
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An Efficient movie recommendation algorithm based on improved k-clique
... the k‑clique methodology used to analyze social networks to be the guidance of this ...recommendation algorithm based on improved k‑clique methods which are the best accuracy of the recommendation ...
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IJCSMC, Vol. 6, Issue. 1, January 2017, pg.43 – 51 A Weighted Voting of K-Nearest Neighbor Algorithm for Diabetes Mellitus
... of Nearest Neighbor classification [5]: 1) Choosing Factor K: One of the parameters to choose is the value of ...for K is pre-selected and the optimal value of K depends on the size and nature ...
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Local K-Nearest Neighbors Model using Z-Order R-Tree for Big Data
... Our algorithm proves best on uniformly distributed data as well as non-uniformly distributed ...ZR-KNN algorithm is evaluated using both synthetic and actual data from various application ...
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Hybrid Nearest Neighbors Ant Colony Optimization for Clustering Social Media Comments
... the algorithm spends more time to ...with k- means algorithm. K-means is used to generate the initial solution to be explored later by ...the algorithm is only tested for processing the ...
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A Novel Hybrid Approach for Email Spam Detection based on Scatter Search Algorithm and K-Nearest Neighbors
... on k closest training examples in feature space [27, ...among k neighbor are calculated, the label which possesses majority to be to the unknown sample will be ...
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k-Nearest Neighbors Queries in Time-Dependent Road Networks
... our algorithm, the heuristic function adds to each vertex an estimate of the potential for it to take part of the fastest path that leads to a nearest point of ...next nearest POI when expanding ...
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