Slip prediction with the k-Nearest Neighbor algorithm
Comparative Analysis of K-Nearest Neighbor and Modified K-Nearest Neighbor Algorithm for Data Classification
... Modified K-Nearest Neighbor (MKNN) classification technique which is derived from classification algorithm of KNN by increasing the calculation of validity and weight ...the nearest ...
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An Infectious Disease Prediction Method Based on K-Nearest Neighbor Improved Algorithm
... disease prediction models to assist doctors in diagnosis and improve work ...disease prediction method based on k-nearest neighbor improvement algorithm from the perspective of ...
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Diabetes Prediction by Optimizing the Nearest Neighbor Algorithm Using Genetic Algorithm
... 3. Gastaldelli A, Gaggini M, DeFronzo RA. Role of adipose tissue insulin resistance in the natural history of type 2 diabetes: results from the san antonio metabolism study. Diabetes 2017;66(4):815-22. 4. Volkov P, Bacos ...
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A Deterministic K means Algorithm based on Nearest Neighbor Search
... In the following section we start with a brief description of the k-means algorithm and a formal definition of the clustering SSE error, then we describe the proposed KMNN algorithm. Section 3 ...
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K-Nearest neighbor algorithm on implicit feedback to determine SOP
... The k-nearest neighbor (KNN) algorithm is used to identify the most relevant SOP document for the user by utilizing implicit feedback based on extraction data by monitoring the document search ...
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A Customer Churn Prediction using Pearson Correlation Function and K Nearest Neighbor Algorithm for Telecommunication Industry
... churn prediction’. It is critical to implement the churn prediction in their approach to forecast high risk ...the algorithm automatically without human ...churn prediction model is churn and ...
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Enhancement of student performance prediction using modified K-nearest neighbor
... FKNN algorithm which combines the sample indexing mechanism with KNN to deal with the major problems of the traditional ...proposed algorithm was very useful for the real-time system such as e learning ...
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Identification of Canola Seeds using Nearest Neighbor and K-Nearest Neighbor Algorithms
... (SIMCA), K-Nearest Neighbor Algorithm (KNN) and Support Vector Machine (SVM), a novel machine learning calculation called Random Forest (RF) was connected in this test ...
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Improve Adaptive k-Nearest Neighbor Algorithm using Multi-threading
... of k-nearest neighbour algorithm also depends upon choice of ...of k is small, and noise is present in the pattern space, then noisy samples can win the majority votes, which results into ...
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A Modification on K-Nearest Neighbor Classifier
... value K, over large variety of ...KNN algorithm in performance. Fig 2 investigates the effect of parameter K on accuracy of algorithms KNN and MKNN comparatively in four different data sets: Iris, ...
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An Improved K-nearest Neighbor Model for Short-term Traffic Flow Prediction
... a k-nearest neighbor (KNN) ...flow prediction system based on k-NN is established in three aspects: the historical database, the search mechanism and algorithm parameters, and ...
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Evaluation Of The Performance Of K-Nearest Neighbor Algorithm In Determining Student Learning Styles
... static algorithm only accommodates what it is instructed to ...Therefore, K-NN algorithm is used in the domain of students’ learning styles to predict the possible outcomes using various number of ...
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Credit card fraud detection using anti-k nearest neighbor algorithm
... Our experiments were performed on several synthetic data and real data sets. In all our experiments, we have assumed that we have information about the outliers in the data set, so we could evaluate the detection ...
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Prediction of Compressive Strength in High Performance Concrete with Hooked-End Steel Fiber using K-Nearest Neighbor Algorithm
... parameter k) a K-Nearest Neighbor algorithm was put to test in high performance concrete (HPC) with steel fiber ...with K =1 nearest neighbor and 3 split ...
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K-nearest neighbor search for fuzzy objects
... The K-Nearest Neighbor search (kNN) problem has been investi- gated extensively in the past due to its broad range of ...the k nearest objects qualifying at a probability threshold or ...
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Simultaneous interval regression for K-nearest neighbor
... a prediction variable like prediction intervals, tolerance intervals, confidence intervals of quantiles and si- multaneous confidence intervals of quantiles, ...the prediction variable Y given any ...
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Prediction Using Back Propagation and k-Nearest Neighbour (k-NN) Algorithm
... mining, k-NN, neural network, prediction, stock ...price prediction is an interesting topic for research purposes as well as marketable field, in many developed country power cost-cutting measure is ...
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An Improved Nearest Neighbor Algorithm for Solving TSP
... Distances between each city are given. Each city represents as node and their distance represents as arc. A salesman needs to visit each and every city exactly once without making any repetition then come back to the ...
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New Applications of the Nearest-Neighbor Chain Algorithm
... 1.1 Greedy algorithms The greedy strategy is one of the most ubiquitous algorithm design paradigms (e.g., see [61, 104]). Greedy algorithms can be used for a broad class of optimization problems, but with some ...
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What Affects K Value Selection In K-Nearest Neighbor
... Abstract: k-Nearest neighbor is a popular classifier and has been applied in many ...of k value. Different k values can have a large impact on the predictive accuracy of the ...
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