Relevant bibliographies by topics / K-Nearest Neighbors algorithm

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Academic literature on the topic 'K-Nearest Neighbors algorithm'

Author: Grafiati
Published: 4 June 2021
Last updated: 27 April 2022

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Journal articles on the topic "K-Nearest Neighbors algorithm"

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Zhai, Junhai, Jiaxing Qi, and Sufang Zhang. "An instance selection algorithm for fuzzy K-nearest neighbor." Journal of Intelligent & Fuzzy Systems 40, no. 1 (January 4, 2021): 521–33. http://dx.doi.org/10.3233/jifs-200124.

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The condensed nearest neighbor (CNN) is a pioneering instance selection algorithm for 1-nearest neighbor. Many variants of CNN for K-nearest neighbor have been proposed by different researchers. However, few studies were conducted on condensed fuzzy K-nearest neighbor. In this paper, we present a condensed fuzzy K-nearest neighbor (CFKNN) algorithm that starts from an initial instance set S and iteratively selects informative instances from training set T, moving them from T to S. Specifically, CFKNN consists of three steps. First, for each instance x ∈ T, it finds the K-nearest neighbors in S and calculates the fuzzy membership degrees of the K nearest neighbors using S rather than T. Second it computes the fuzzy membership degrees of x using the fuzzy K-nearest neighbor algorithm. Finally, it calculates the information entropy of x and selects an instance according to the calculated value. Extensive experiments on 11 datasets are conducted to compare CFKNN with four state-of-the-art algorithms (CNN, edited nearest neighbor (ENN), Tomeklinks, and OneSidedSelection) regarding the number of selected instances, the testing accuracy, and the compression ratio. The experimental results show that CFKNN provides excellent performance and outperforms the other four algorithms.
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Houben, I., L. Wehenkel, and M. Pavella. "Genetic Algorithm Based k Nearest Neighbors." IFAC Proceedings Volumes 30, no. 6 (May 1997): 1075–80. http://dx.doi.org/10.1016/s1474-6670(17)43506-3.

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Onyezewe, Anozie, Armand F. Kana, Fatimah B. Abdullahi, and Aminu O. Abdulsalami. "An Enhanced Adaptive k-Nearest Neighbor Classifier Using Simulated Annealing." International Journal of Intelligent Systems and Applications 13, no. 1 (February 8, 2021): 34–44. http://dx.doi.org/10.5815/ijisa.2021.01.03.

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The k-Nearest Neighbor classifier is a non-complex and widely applied data classification algorithm which does well in real-world applications. The overall classification accuracy of the k-Nearest Neighbor algorithm largely depends on the choice of the number of nearest neighbors(k). The use of a constant k value does not always yield the best solutions especially for real-world datasets with an irregular class and density distribution of data points as it totally ignores the class and density distribution of a test point’s k-environment or neighborhood. A resolution to this problem is to dynamically choose k for each test instance to be classified. However, given a large dataset, it becomes very tasking to maximize the k-Nearest Neighbor performance by tuning k. This work proposes the use of Simulated Annealing, a metaheuristic search algorithm, to select optimal k, thus eliminating the prospect of an exhaustive search for optimal k. The results obtained in four different classification tasks demonstrate a significant improvement in the computational efficiency against the k-Nearest Neighbor methods that perform exhaustive search for k, as accurate nearest neighbors are returned faster for k-Nearest Neighbor classification, thus reducing the computation time.
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Piegl, Les A., and Wayne Tiller. "Algorithm for finding all k nearest neighbors." Computer-Aided Design 34, no. 2 (February 2002): 167–72. http://dx.doi.org/10.1016/s0010-4485(00)00141-x.

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Tu, Ching Ting, Hsiau Wen Lin, Hwei-Jen Lin, and Yue Shen Li. "Super-Resolution Based on Clustered Examples." International Journal of Pattern Recognition and Artificial Intelligence 30, no. 06 (May 9, 2016): 1655015. http://dx.doi.org/10.1142/s0218001416550156.

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In this paper, we propose an improved version of the neighbor embedding super-resolution (SR) algorithm proposed by Chang et al. [Super-resolution through neighbor embedding, in Proc. 2004 IEEE Computer Society Conf. Computer Vision and Pattern Recognition(CVPR), Vol. 1 (2004), pp. 275–282]. The neighbor embedding SR algorithm requires intensive computational time when finding the K nearest neighbors for the input patch in a huge set of training samples. We tackle this problem by clustering the training sample into a number of clusters, with which we first find for the input patch the nearest cluster center, and then find the K nearest neighbors in the corresponding cluster. In contrast to Chang’s method, which uses Euclidean distance to find the K nearest neighbors of a low-resolution patch, we define a similarity function and use that to find the K most similar neighbors of a low-resolution patch. We then use local linear embedding (LLE) [S. T. Roweis and L. K. Saul, Nonlinear dimensionality reduction by locally linear embedding, Science 290(5500) (2000) 2323–2326] to find optimal coefficients, with which the linear combination of the K most similar neighbors best approaches the input patch. These coefficients are then used to form a linear combination of the K high-frequency patches corresponding to the K respective low-resolution patches (or the K most similar neighbors). The resulting high-frequency patch is then added to the enlarged (or up-sampled) version of the input patch. Experimental results show that the proposed clustering scheme efficiently reduces computational time without significantly affecting the performance.
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Song, Yunsheng, Xiaohan Kong, and Chao Zhang. "A Large-Scale k -Nearest Neighbor Classification Algorithm Based on Neighbor Relationship Preservation." Wireless Communications and Mobile Computing 2022 (January 7, 2022): 1–11. http://dx.doi.org/10.1155/2022/7409171.

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Owing to the absence of hypotheses of the underlying distributions of the data and the strong generation ability, the k -nearest neighbor (kNN) classification algorithm is widely used to face recognition, text classification, emotional analysis, and other fields. However, kNN needs to compute the similarity between the unlabeled instance and all the training instances during the prediction process; it is difficult to deal with large-scale data. To overcome this difficulty, an increasing number of acceleration algorithms based on data partition are proposed. However, they lack theoretical analysis about the effect of data partition on classification performance. This paper has made a theoretical analysis of the effect using empirical risk minimization and proposed a large-scale k -nearest neighbor classification algorithm based on neighbor relationship preservation. The process of searching the nearest neighbors is converted to a constrained optimization problem. Then, it gives the estimation of the difference on the objective function value under the optimal solution with data partition and without data partition. According to the obtained estimation, minimizing the similarity of the instances in the different divided subsets can largely reduce the effect of data partition. The minibatch k -means clustering algorithm is chosen to perform data partition for its effectiveness and efficiency. Finally, the nearest neighbors of the test instance are continuously searched from the set generated by successively merging the candidate subsets until they do not change anymore, where the candidate subsets are selected based on the similarity between the test instance and cluster centers. Experiment results on public datasets show that the proposed algorithm can largely keep the same nearest neighbors and no significant difference in classification accuracy as the original kNN classification algorithm and better results than two state-of-the-art algorithms.
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Prasetio, Rizki Tri, Ali Akbar Rismayadi, and Iedam Fardian Anshori. "Implementasi Algoritma Genetika pada k-nearest neighbours untuk Klasifikasi Kerusakan Tulang Belakang." Jurnal Informatika 5, no. 2 (September 29, 2018): 186–94. http://dx.doi.org/10.31311/ji.v5i2.4123.

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AbstrakKerusakan tulang belakang dialami oleh sekitar dua pertiga orang dewasa serta termasuk ke dalam penyakit yang paling umum kedua setelah sakit kepala. Klasifikasi gangguan tulang belakang sulit dilakukan karena membutuhkan radiologist untuk menganalisa citra Magnetic Resonance Imaging (MRI). Penggunaan Computer Aided Diagnosis (CAD) System dapat membantu radiologist untuk mendeteksi kelainan pada tulang belakang dengan lebih optimal. Dataset vertebral column memiliki tiga kelas sebagai klasifikasi penyakit kerusakan tulang belakang yaitu, herniated disk, spondylolisthesis dan kelas normal yang diambil berdasarkan hasil ekstraksi citra MRI. Dataset akan diolah dalam lima eksperimen berdasarkan validasi dataset menggunakan split validation dengan pembagian data training dan data testing yang bervariasi. Pada penelitian ini diusulkan implementasi algoritma genetika pada algoritma k-nearest neighbours untuk meningkatkan akurasi dari klasifikasi gangguan tulang belakang. Algoritma genetika digunakan untuk fitur seleksi dan optimasi parameter algoritma k-nearest neighbours. Hasil penelitian menunjukan bahwa metode yang diusulkan menghasilkan peningkatan yang signifikan dalam klasifikasi kerusakan pada tulang belakang. Metode yang diusulkan menghasilkan rata-rata akurasi sebesar 93% dari lima eksperimen. Hasil ini lebih baik dari algoritma k-nearest neighbours yang menghasilkan rata-rata akurasi hanya sebesar 82.54%. Kata kunci: algoritma genetika, k-nearest neighbours, kerusakan tulang belakang, vertebral AbstractSpinal disorder is experienced by about two-thirds of adults and is included in the second most common disease after headaches. Classification of spinal disorders is difficult because it requires a radiologist to analyze Magnetic Resonance Imaging (MRI) images. The use of Computer Aided Diagnosis (CAD) System can help radiologists to detect abnormalities in the spine more optimally. The vertebral column dataset has three classes as a classification of spinal disorders, namely, herniated disk, spondylolisthesis and normal classes taken based on MRI Image extraction. The dataset will be processed in five experiments based on dataset validation using split validation with various training data and testing data. In this study proposed the implementation of genetic algorithms in the k-nearest neighbors algorithm to improve the accuracy of the classification of spinal disorders. Genetic algorithms are used for algorithm feature selection and parameter optimization of k-nearest neighbors. The results showed that the proposed method produced a significant increase in the classification of spinal disorder. The proposed method produces an average accuracy of 93% from five experiments. This result is better than the k-nearest neighbors algorithm which produces an average accuracy of only 82.54%. Keywords: genetic algorithm, k-nearest neighbours, spinal disorder, vertebral column.
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Prasetio, Rizki Tri, Ali Akbar Rismayadi, and Iedam Fardian Anshori. "Implementasi Algoritma Genetika pada k-nearest neighbours untuk Klasifikasi Kerusakan Tulang Belakang." Jurnal Informatika 5, no. 2 (September 29, 2018): 186–94. http://dx.doi.org/10.31294/ji.v5i2.4123.

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AbstrakKerusakan tulang belakang dialami oleh sekitar dua pertiga orang dewasa serta termasuk ke dalam penyakit yang paling umum kedua setelah sakit kepala. Klasifikasi gangguan tulang belakang sulit dilakukan karena membutuhkan radiologist untuk menganalisa citra Magnetic Resonance Imaging (MRI). Penggunaan Computer Aided Diagnosis (CAD) System dapat membantu radiologist untuk mendeteksi kelainan pada tulang belakang dengan lebih optimal. Dataset vertebral column memiliki tiga kelas sebagai klasifikasi penyakit kerusakan tulang belakang yaitu, herniated disk, spondylolisthesis dan kelas normal yang diambil berdasarkan hasil ekstraksi citra MRI. Dataset akan diolah dalam lima eksperimen berdasarkan validasi dataset menggunakan split validation dengan pembagian data training dan data testing yang bervariasi. Pada penelitian ini diusulkan implementasi algoritma genetika pada algoritma k-nearest neighbours untuk meningkatkan akurasi dari klasifikasi gangguan tulang belakang. Algoritma genetika digunakan untuk fitur seleksi dan optimasi parameter algoritma k-nearest neighbours. Hasil penelitian menunjukan bahwa metode yang diusulkan menghasilkan peningkatan yang signifikan dalam klasifikasi kerusakan pada tulang belakang. Metode yang diusulkan menghasilkan rata-rata akurasi sebesar 93% dari lima eksperimen. Hasil ini lebih baik dari algoritma k-nearest neighbours yang menghasilkan rata-rata akurasi hanya sebesar 82.54%. Kata kunci: algoritma genetika, k-nearest neighbours, kerusakan tulang belakang, vertebral AbstractSpinal disorder is experienced by about two-thirds of adults and is included in the second most common disease after headaches. Classification of spinal disorders is difficult because it requires a radiologist to analyze Magnetic Resonance Imaging (MRI) images. The use of Computer Aided Diagnosis (CAD) System can help radiologists to detect abnormalities in the spine more optimally. The vertebral column dataset has three classes as a classification of spinal disorders, namely, herniated disk, spondylolisthesis and normal classes taken based on MRI Image extraction. The dataset will be processed in five experiments based on dataset validation using split validation with various training data and testing data. In this study proposed the implementation of genetic algorithms in the k-nearest neighbors algorithm to improve the accuracy of the classification of spinal disorders. Genetic algorithms are used for algorithm feature selection and parameter optimization of k-nearest neighbors. The results showed that the proposed method produced a significant increase in the classification of spinal disorder. The proposed method produces an average accuracy of 93% from five experiments. This result is better than the k-nearest neighbors algorithm which produces an average accuracy of only 82.54%. Keywords: genetic algorithm, k-nearest neighbours, spinal disorder, vertebral column.
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Li, Xiaoguang. "Research and Implementation of Digital Media Recommendation System Based on Semantic Classification." Advances in Multimedia 2022 (March 27, 2022): 1–6. http://dx.doi.org/10.1155/2022/4070827.

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In order to study the recommendation system of digital media based on semantic classification, the CF-LFMC algorithm based on semantic classification is proposed. Firstly, the traditional algorithm is analyzed. Aiming at some problems existing in the traditional algorithm, a clustering algorithm model based on term meaning and collaborative filtering algorithm is designed by combining the collaborative filtering algorithm and project-based clustering algorithm. Before analyzing sparse data, the cold start and timeliness of the traditional algorithm are improved. Secondly, the performance comparison of three cosine similarity calculation methods of experimental IBCF algorithm, the performance comparison between CF-LFMC algorithm and IBCF algorithm, and the performance comparison between CF-LFMC algorithm and CF-LFMC algorithm without the time function is carried out. The clustering value N = 10 in the CF-LFMC algorithm is taken as the experimental result; MAE values of both algorithms decrease with the increase of the nearest neighbor number k. When the number of nearest neighbors is small, MAE values of the two algorithms are close to each other. As the number of nearest neighbors increases, the accuracy of the algorithm does not improve significantly, and the calculation cost of the algorithm will increase with the increase of the number of nearest neighbors, so the number of nearest neighbors between 20 and 30 is more appropriate. CF-LFMC shows better accuracy, and the CF-LFMC algorithm improved by the time function has improved the accuracy, which is better than the traditional algorithm in accuracy.
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Prasad, Devendra, Sandip Kumar Goyal, Avinash Sharma, Amit Bindal, and Virendra Singh Kushwah. "System Model for Prediction Analytics Using K-Nearest Neighbors Algorithm." Journal of Computational and Theoretical Nanoscience 16, no. 10 (October 1, 2019): 4425–30. http://dx.doi.org/10.1166/jctn.2019.8536.

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Machine Learning is a growing area in computer science in today’s era. This article is focusing on prediction analysis using K-Nearest Neighbors (KNN) Machine Learning algorithm. Data in the dataset are processed, analyzed and predicated using the specified algorithm. Introduction of various Machine Learning algorithms, its pros and cons have been discussed. The KNN algorithm with detail study is given and it is implemented on the specified data with certain parameters. The research work elucidates prediction analysis and explicates the prediction of quality of restaurants.
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Dissertations / Theses on the topic "K-Nearest Neighbors algorithm"

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Li, Zheng, and Zheng Li. "Improving Estimation Accuracy of GPS-Based Arterial Travel Time Using K-Nearest Neighbors Algorithm." Thesis, The University of Arizona, 2017. http://hdl.handle.net/10150/625901.

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Link travel time plays a significant role in traffic planning, traffic management and Advanced Traveler Information Systems (ATIS). A public probe vehicle dataset is a probe vehicle dataset that is collected from public people or public transport. The appearance of public probe vehicle datasets can support travel time collection at a large temporal and spatial scale but at a relatively low cost. Traditionally, link travel time is the aggregation of travel time by different movements. A recent study proved that link travel time of different movements is significantly different from their aggregation. However, there is still not a complete framework for estimating movement-based link travel time. In addition, probe vehicle datasets usually have a low penetration rate but no previous study has solved this problem. To solve the problems above, this study proposed a detailed framework to estimate movement-based link travel time using a high sampling rate public probe vehicle dataset. Our study proposed a k-Nearest Neighbors (k-NN) regression method to increase travel time samples using incomplete trajectory. An incomplete trajectory was compared with historical complete trajectories and the link travel time of the incomplete trajectory was represented by its similar complete trajectories. The result of our study showed that the method can significantly increase link travel time samples but there are still limitations. In addition, our study investigated the performance of k-NN regression under different parameters and input data. The sensitivity analysis of k-NN algorithm showed that the algorithm performed differently under different parameters and input data. Our study suggests optimal parameters should be selected using a historical dataset before real-world application.
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Piro, Paolo. "Learning prototype-based classification rules in a boosting framework: application to real-world and medical image categorization." Phd thesis, Université de Nice Sophia-Antipolis, 2010. http://tel.archives-ouvertes.fr/tel-00590403.

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Gupta, Nidhi. "Mutual k Nearest Neighbor based Classifier." University of Cincinnati / OhioLINK, 2010. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1289937369.

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Olivares, Javier. "Scaling out-of-core k-nearest neighbors computation on single machines." Thesis, Rennes 1, 2016. http://www.theses.fr/2016REN1S073/document.

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La technique des K-plus proches voisins (K-Nearest Neighbors (KNN) en Anglais) est une méthode efficace pour trouver des données similaires au sein d'un grand ensemble de données. Au fil des années, un grand nombre d'applications ont utilisé les capacités du KNN pour découvrir des similitudes dans des jeux de données de divers domaines tels que les affaires, la médecine, la musique, ou l'informatique. Bien que des années de recherche aient apporté plusieurs approches de cet algorithme, sa mise en œuvre reste un défi, en particulier aujourd'hui alors que les quantités de données croissent à des vitesses inimaginables. Dans ce contexte, l'exécution du KNN sur de grands ensembles pose deux problèmes majeurs: d'énormes empreintes mémoire et de très longs temps d'exécution. En raison de ces coût élevés en termes de ressources de calcul et de temps, les travaux de l'état de l'art ne considèrent pas le fait que les données peuvent changer au fil du temps, et supposent toujours que les données restent statiques tout au long du calcul, ce qui n'est malheureusement pas du tout conforme à la réalité. Nos contributions dans cette thèse répondent à ces défis. Tout d'abord, nous proposons une approche out-of-core pour calculer les KNN sur de grands ensembles de données en utilisant un seul ordinateur. Nous préconisons cette approche comme un moyen moins coûteux pour faire passer à l'échelle le calcul des KNN par rapport au coût élevé d'un algorithme distribué, tant en termes de ressources de calcul que de temps de développement, de débogage et de déploiement. Deuxièmement, nous proposons une approche out-of-core multithreadée (i.e. utilisant plusieurs fils d'exécution) pour faire face aux défis du calcul des KNN sur des données qui changent rapidement et continuellement au cours du temps. Après une évaluation approfondie, nous constatons que nos principales contributions font face aux défis du calcul des KNN sur de grands ensembles de données, en tirant parti des ressources limitées d'une machine unique, en diminuant les temps d'exécution par rapport aux performances actuelles, et en permettant le passage à l'échelle du calcul, à la fois sur des données statiques et des données dynamiques
The K-Nearest Neighbors (KNN) is an efficient method to find similar data among a large set of it. Over the years, a huge number of applications have used KNN's capabilities to discover similarities within the data generated in diverse areas such as business, medicine, music, and computer science. Despite years of research have brought several approaches of this algorithm, its implementation still remains a challenge, particularly today where the data is growing at unthinkable rates. In this context, running KNN on large datasets brings two major issues: huge memory footprints and very long runtimes. Because of these high costs in terms of computational resources and time, KNN state-of the-art works do not consider the fact that data can change over time, assuming always that the data remains static throughout the computation, which unfortunately does not conform to reality at all. In this thesis, we address these challenges in our contributions. Firstly, we propose an out-of-core approach to compute KNN on large datasets, using a commodity single PC. We advocate this approach as an inexpensive way to scale the KNN computation compared to the high cost of a distributed algorithm, both in terms of computational resources as well as coding, debugging and deployment effort. Secondly, we propose a multithreading out-of-core approach to face the challenges of computing KNN on data that changes rapidly and continuously over time. After a thorough evaluation, we observe that our main contributions address the challenges of computing the KNN on large datasets, leveraging the restricted resources of a single machine, decreasing runtimes compared to that of the baselines, and scaling the computation both on static and dynamic datasets
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Wong, Wing Sing. "K-nearest-neighbor queries with non-spatial predicates on range attributes /." View abstract or full-text, 2005. http://library.ust.hk/cgi/db/thesis.pl?COMP%202005%20WONGW.

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Aikes, Junior Jorge. "Estudo da influência de diversas medidas de similaridade na previsão de séries temporais utilizando o algoritmo KNN-TSP." Universidade Estadual do Oeste do Parana, 2012. http://tede.unioeste.br:8080/tede/handle/tede/1084.

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Made available in DSpace on 2017-07-10T17:11:50Z (GMT). No. of bitstreams: 1 JORGE AIKES JUNIOR.PDF: 2050278 bytes, checksum: f5bae18bbcb7465240488c45b2c813e7 (MD5) Previous issue date: 2012-04-11
Time series can be understood as any set of observations which are time ordered. Among the many possible tasks appliable to temporal data, one that has attracted increasing interest, due to its various applications, is the time series forecasting. The k-Nearest Neighbor - Time Series Prediction (kNN-TSP) algorithm is a non-parametric method for forecasting time series. One of its advantages, is its easiness application when compared to parametric methods. Even though its easier to define kNN-TSP s parameters, some issues remain opened. This research is focused on the study of one of these parameters: the similarity measure. This parameter was empirically evaluated using various similarity measures in a large set of time series, including artificial series with seasonal and chaotic characteristics, and several real world time series. It was also carried out a case study comparing the predictive accuracy of the kNN-TSP algorithm with the Moving Average (MA), univariate Seasonal Auto-Regressive Integrated Moving Average (SARIMA) and multivariate SARIMA methods in a time series of a Korean s hospital daily patients flow in the Emergency Department. This work also proposes an approach to the development of a hybrid similarity measure which combines characteristics from several measures. The research s result demonstrated that the Lp Norm s measures have an advantage over other measures evaluated, due to its lower computational cost and for providing, in general, greater accuracy in temporal data forecasting using the kNN-TSP algorithm. Although the literature in general adopts the Euclidean similarity measure to calculate de similarity between time series, the Manhattan s distance can be considered an interesting candidate for defining similarity, due to the absence of statistical significant difference and to its lower computational cost when compared to the Euclidian measure. The measure proposed in this work does not show significant results, but it is promising for further research. Regarding the case study, the kNN-TSP algorithm with only the similarity measure parameter optimized achieves a considerably lower error than the MA s best configuration, and a slightly greater error than the univariate e multivariate SARIMA s optimal settings presenting less than one percent of difference.
Séries temporais podem ser entendidas como qualquer conjunto de observações que se encontram ordenadas no tempo. Dentre as várias tarefas possíveis com dados temporais, uma que tem atraído crescente interesse, devido a suas várias aplicações, é a previsão de séries temporais. O algoritmo k-Nearest Neighbor - Time Series Prediction (kNN-TSP) é um método não-paramétrico de previsão de séries temporais que apresenta como uma de suas vantagens a facilidade de aplicação, quando comparado aos métodos paramétricos. Apesar da maior facilidade na determinação de seus parâmetros, algumas questões relacionadas continuam em aberto. Este trabalho está focado no estudo de um desses parâmetros: a medida de similaridade. Esse parâmetro foi avaliado empiricamente utilizando diversas medidas de similaridade em um grande conjunto de séries temporais que incluem séries artificiais, com características sazonais e caóticas, e várias séries reais. Foi realizado também um estudo de caso comparativo entre a precisão da previsão do algoritmo kNN-TSP e a dos métodos de Médias Móveis (MA), Auto-regressivos de Médias Móveis Integrados Sazonais (SARIMA) univariado e SARIMA multivariado, em uma série de fluxo diário de pacientes na Área de Emergência de um hospital coreano. Neste trabalho é ainda proposta uma abordagem para o desenvolvimento de uma medida de similaridade híbrida, que combine características de várias medidas. Os resultados obtidos neste trabalho demonstram que as medidas da Norma Lp apresentam vantagem sobre as demais medidas avaliadas, devido ao seu menor custo computacional e por apresentar, em geral, maior precisão na previsão de dados temporais utilizando o algoritmo kNN-TSP. Apesar de na literatura, em geral, a medida Euclidiana ser adotada como medida de similaridade, a medida Manhattan pode ser considerada candidata interessante para definir a similaridade entre séries temporais, devido a não apresentar diferença estatisticamente significativa com a medida Euclidiana e possuir menor custo computacional. A medida proposta neste trabalho, não apresenta resultados significantes, mas apresenta-se promissora para novas pesquisas. Com relação ao estudo de caso, o algoritmo kNN-TSP, com apenas o parâmetro de medida de similaridade otimizado, alcança um erro consideravelmente inferior a melhor configuração com MA, e pouco maior que as melhores configurações dos métodos SARIMA univariado e SARIMA multivariado, sendo essa diferença inferior a um por cento.
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Johansson, David. "Price Prediction of Vinyl Records Using Machine Learning Algorithms." Thesis, Linnéuniversitetet, Institutionen för datavetenskap och medieteknik (DM), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:lnu:diva-96464.

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Machine learning algorithms have been used for price prediction within several application areas. Examples include real estate, the stock market, tourist accommodation, electricity, art, cryptocurrencies, and fine wine. Common approaches in studies are to evaluate the accuracy of predictions and compare different algorithms, such as Linear Regression or Neural Networks. There is a thriving global second-hand market for vinyl records, but the research of price prediction within the area is very limited. The purpose of this project was to expand on existing knowledge within price prediction in general to evaluate some aspects of price prediction of vinyl records. That included investigating the possible level of accuracy and comparing the efficiency of algorithms. A dataset of 37000 samples of vinyl records was created with data from the Discogs website, and multiple machine learning algorithms were utilized in a controlled experiment. Among the conclusions drawn from the results was that the Random Forest algorithm generally generated the strongest results, that results can vary substantially between different artists or genres, and that a large part of the predictions had a good accuracy level, but that a relatively small amount of large errors had a considerable effect on the general results.
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Mestre, Ricardo Jorge Palheira. "Improvements on the KNN classifier." Master's thesis, Faculdade de Ciências e Tecnologia, 2013. http://hdl.handle.net/10362/10923.

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Dissertação para obtenção do Grau de Mestre em Engenharia Informática
The object classification is an important area within the artificial intelligence and its application extends to various areas, whether or not in the branch of science. Among the other classifiers, the K-nearest neighbor (KNN) is among the most simple and accurate especially in environments where the data distribution is unknown or apparently not parameterizable. This algorithm assigns the classifying element the major class in the K nearest neighbors. According to the original algorithm, this classification implies the calculation of the distances between the classifying instance and each one of the training objects. If on the one hand, having an extensive training set is an element of importance in order to obtain a high accuracy, on the other hand, it makes the classification of each object slower due to its lazy-learning algorithm nature. Indeed, this algorithm does not provide any means of storing information about the previous calculated classifications,making the calculation of the classification of two equal instances mandatory. In a way, it may be said that this classifier does not learn. This dissertation focuses on the lazy-learning fragility and intends to propose a solution that transforms the KNNinto an eager-learning classifier. In other words, it is intended that the algorithm learns effectively with the training set, thus avoiding redundant calculations. In the context of the proposed change in the algorithm, it is important to highlight the attributes that most characterize the objects according to their discriminating power. In this framework, there will be a study regarding the implementation of these transformations on data of different types: continuous and/or categorical.
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Liu, Dongqing. "GENETIC ALGORITHMS FOR SAMPLE CLASSIFICATION OF MICROARRAY DATA." University of Akron / OhioLINK, 2005. http://rave.ohiolink.edu/etdc/view?acc_num=akron1125253420.

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Neo, TohKoon. "A Direct Algorithm for the K-Nearest-Neighbor Classifier via Local Warping of the Distance Metric." Diss., CLICK HERE for online access, 2007. http://contentdm.lib.byu.edu/ETD/image/etd2168.pdf.

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Book chapters on the topic "K-Nearest Neighbors algorithm"

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Rekha Sundari, M., G. Siva Rama Krishna, V. Sai Naveen, and G. Bharathi. "Crop Recommendation System Using K-Nearest Neighbors Algorithm." In Proceedings of 6th International Conference on Recent Trends in Computing, 581–89. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-33-4501-0_54.

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Agarwal, Pankaj K., and Sandeep Sen. "Selection in monotone matrices and computing k th nearest neighbors." In Algorithm Theory — SWAT '94, 13–24. Berlin, Heidelberg: Springer Berlin Heidelberg, 1994. http://dx.doi.org/10.1007/3-540-58218-5_2.

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Hamraz, Seyed Hamid, and Seyed Shams Feyzabadi. "General-Purpose Learning Machine Using K-Nearest Neighbors Algorithm." In RoboCup 2005: Robot Soccer World Cup IX, 529–36. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11780519_50.

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Wang, Xiaochun, Xiali Wang, and Don Mitchell Wilkes. "A New Fast K-Nearest Neighbors-Based Clustering Algorithm." In Machine Learning-based Natural Scene Recognition for Mobile Robot Localization in An Unknown Environment, 129–51. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-9217-7_7.

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Yin, Shihao, Runxiu Wu, Peiwu Li, Baohong Liu, and Xuefeng Fu. "Density Peaks Clustering Algorithm Based on K Nearest Neighbors." In Advances in Intelligent Systems and Computing, 129–44. Singapore: Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-8048-9_13.

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Rathore, Manjari Singh, Praneet Saurabh, Ritu Prasad, and Pradeep Mewada. "Text Classification with K-Nearest Neighbors Algorithm Using Gain Ratio." In Advances in Intelligent Systems and Computing, 23–31. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-2414-1_3.

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Guarracino, Mario R., and Adriano Nebbia. "Predicting Protein-Protein Interactions with K-Nearest Neighbors Classification Algorithm." In Computational Intelligence Methods for Bioinformatics and Biostatistics, 139–50. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-14571-1_10.

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Goswami, Partha P., Sandip Das, and Subhas C. Nandy. "Simplex Range Searching and k Nearest Neighbors of a Line Segment in 2D." In Algorithm Theory — SWAT 2002, 69–79. Berlin, Heidelberg: Springer Berlin Heidelberg, 2002. http://dx.doi.org/10.1007/3-540-45471-3_8.

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Zhang, Yan, Yan Jia, Xiaobin Huang, Bin Zhou, and Jian Gu. "An Adaptive k-Nearest Neighbors Clustering Algorithm for Complex Distribution Dataset." In Advanced Intelligent Computing Theories and Applications. With Aspects of Artificial Intelligence, 398–407. Berlin, Heidelberg: Springer Berlin Heidelberg, 2007. http://dx.doi.org/10.1007/978-3-540-74205-0_44.

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Agrawal, Rashmi. "Integrated Effect of Nearest Neighbors and Distance Measures in k-NN Algorithm." In Advances in Intelligent Systems and Computing, 759–66. Singapore: Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-6620-7_74.

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Conference papers on the topic "K-Nearest Neighbors algorithm"

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Gauza, Dariusz, Anna Żukowska, and Robert Nowak. "K-nearest neighbors clustering algorithm." In Symposium on Photonics Applications in Astronomy, Communications, Industry and High-Energy Physics Experiments, edited by Ryszard S. Romaniuk. SPIE, 2014. http://dx.doi.org/10.1117/12.2074124.

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LI, CHENGJIE, ZHENG PEI, BO LI, and ZHEN ZHANG. "A NEW FUZZY K-NEAREST NEIGHBORS ALGORITHM." In Proceedings of the 4th International ISKE Conference on Intelligent Systems and Knowledge Engineering. WORLD SCIENTIFIC, 2009. http://dx.doi.org/10.1142/9789814295062_0039.

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Yunlong Gao, Jin-yan Pan, and Feng Gao. "Improved boosting algorithm through weighted k-nearest neighbors classifier." In 2010 3rd IEEE International Conference on Computer Science and Information Technology (ICCSIT 2010). IEEE, 2010. http://dx.doi.org/10.1109/iccsit.2010.5563551.

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Asadi, Meysam, and Kazem Pourhossein. "Locating Renewable Energy Generators Using K-Nearest Neighbors (KNN) Algorithm." In 2019 Iranian Conference on Renewable Energy & Distributed Generation (ICREDG). IEEE, 2019. http://dx.doi.org/10.1109/icredg47187.2019.190179.

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Fang Lu and Qingyuan Bai. "A refined weighted K-Nearest Neighbors algorithm for text categorization." In 2010 IEEE International Conference on Intelligent Systems and Knowledge Engineering (ISKE). IEEE, 2010. http://dx.doi.org/10.1109/iske.2010.5680854.

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Young, Barrington, and Raj Bhatnagar. "Secure algorithm for finding K nearest neighbors in distributed databases." In the 2006 International Conference. New York, New York, USA: ACM Press, 2006. http://dx.doi.org/10.1145/1501434.1501514.

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Yang, Shu-Bo, Julaiti Alafate, Xi Wang, and Zhen Tian. "A Self-Tuning Model Framework Using K-Nearest Neighbors Algorithm." In ASME Turbo Expo 2020: Turbomachinery Technical Conference and Exposition. American Society of Mechanical Engineers, 2020. http://dx.doi.org/10.1115/gt2020-15262.

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Abstract Benchmark models are indispensable for gas turbine engine monitoring and diagnostics. In most cases, benchmark models are used to predict engine data. When the measured data from a real engine does not match up the predicted data, it means that something undesirable is going on and the engine may need servicing. Generally, the benchmark models are also thermodynamic models. They are either offered by engine manufactures or obtained using the technique called “performance adaptation”. In both cases, those models have a major limitation: they cannot reflect the development of engine deterioration over time, i.e., deterioration parameters of these models are fixed unless manually adjusted. In practice, however, most engines deteriorate continuously and gradually over time. Due to the increasing mismatch of deterioration, the accuracy of the benchmark model will get worse gradually. To address the mismatch, this paper presents a self-tuning model framework that intends to improve the existing benchmark models. In the framework, a set of deterioration models are introduced and attached to the original benchmark model. The deterioration models represent the deterioration conditions of all major components. They are machine learning models and can be updated automatically according to the engine measurements. With the aid of the k-nearest neighbours algorithm and gas path analysis techniques, the deterioration models can track the real deterioration conditions through self-tuning and are robust under noise. The proposed model framework is applied to a model turbofan engine. Simulation results show that the accuracy of the benchmark model improves significantly after using the self-tuning framework.
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Gauhar, Noushin, Sunanda Das, and Khadiza Sarwar Moury. "Prediction of Flood in Bangladesh using k-Nearest Neighbors Algorithm." In 2021 2nd International Conference on Robotics, Electrical and Signal Processing Techniques (ICREST). IEEE, 2021. http://dx.doi.org/10.1109/icrest51555.2021.9331199.

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Japa, Arialdis, and Yong Shi. "Parallelizing the Bounded K-Nearest Neighbors Algorithm for Distributed Computing Systems." In 2020 10th Annual Computing and Communication Workshop and Conference (CCWC). IEEE, 2020. http://dx.doi.org/10.1109/ccwc47524.2020.9031198.

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Ling, Wang, and Fu Dong-Mei. "Estimation of Missing Values Using a Weighted K-Nearest Neighbors Algorithm." In 2009 International Conference on Environmental Science and Information Application Technology, ESIAT. IEEE, 2009. http://dx.doi.org/10.1109/esiat.2009.206.

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Reports on the topic "K-Nearest Neighbors algorithm"

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Searcy, Stephen W., and Kalman Peleg. Adaptive Sorting of Fresh Produce. United States Department of Agriculture, August 1993. http://dx.doi.org/10.32747/1993.7568747.bard.

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This project includes two main parts: Development of a “Selective Wavelength Imaging Sensor” and an “Adaptive Classifiery System” for adaptive imaging and sorting of agricultural products respectively. Three different technologies were investigated for building a selectable wavelength imaging sensor: diffraction gratings, tunable filters and linear variable filters. Each technology was analyzed and evaluated as the basis for implementing the adaptive sensor. Acousto optic tunable filters were found to be most suitable for the selective wavelength imaging sensor. Consequently, a selectable wavelength imaging sensor was constructed and tested using the selected technology. The sensor was tested and algorithms for multispectral image acquisition were developed. A high speed inspection system for fresh-market carrots was built and tested. It was shown that a combination of efficient parallel processing of a DSP and a PC based host CPU in conjunction with a hierarchical classification system, yielded an inspection system capable of handling 2 carrots per second with a classification accuracy of more than 90%. The adaptive sorting technique was extensively investigated and conclusively demonstrated to reduce misclassification rates in comparison to conventional non-adaptive sorting. The adaptive classifier algorithm was modeled and reduced to a series of modules that can be added to any existing produce sorting machine. A simulation of the entire process was created in Matlab using a graphical user interface technique to promote the accessibility of the difficult theoretical subjects. Typical Grade classifiers based on k-Nearest Neighbor techniques and linear discriminants were implemented. The sample histogram, estimating the cumulative distribution function (CDF), was chosen as a characterizing feature of prototype populations, whereby the Kolmogorov-Smirnov statistic was employed as a population classifier. Simulations were run on artificial data with two-dimensions, four populations and three classes. A quantitative analysis of the adaptive classifier's dependence on population separation, training set size, and stack length determined optimal values for the different parameters involved. The technique was also applied to a real produce sorting problem, e.g. an automatic machine for sorting dates by machine vision in an Israeli date packinghouse. Extensive simulations were run on actual sorting data of dates collected over a 4 month period. In all cases, the results showed a clear reduction in classification error by using the adaptive technique versus non-adaptive sorting.
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