General-Purpose Learning Machine Using K-Nearest Neighbors Algorithm

2006 ◽  
pp. 529-536 ◽  
Author(s):  
Seyed Hamid Hamraz ◽  
Seyed Shams Feyzabadi
Keyword(s):  
Nearest Neighbors ◽  
General Purpose ◽  
K Nearest Neighbors ◽  
Learning Machine

scholarly journals LexiPers: An ontology based sentiment lexicon for Persian

10.29007/f4j4 ◽  
2018 ◽  
Author(s):  
Behnam Sabeti ◽  
Pedram Hosseini ◽  
Gholamreza Ghassem-Sani ◽  
Sَeyed Abolghasem Mirroshandel
Keyword(s):  
Natural Language Processing ◽  
Language Processing ◽  
Nearest Neighbors ◽  
Classification Problem ◽  
General Purpose ◽  
Seed Selection ◽  
K Nearest Neighbors ◽  
Sentiment Lexicon ◽  
Sentiment Orientation ◽  
F Measure

Sentiment analysis refers to the use of natural language processing to identify and extract subjective information from textual resources. One approach for sentiment extraction is using a sentiment lexicon. A sentiment lexicon is a set of words associated with the sentiment orientation that they express. In this paper, we describe the process of generating a general purpose sentiment lexicon for Persian. A new graph-based method is introduced for seed selection and expansion based on an ontology. Sentiment lexicon generation is then mapped to a document classification problem. We used the K-nearest neighbors and nearest centroid methods for classification. These classifiers have been evaluated based on a set of hand labeled synsets. The final sentiment lexicon has been generated by the best classifier. The results show an acceptable performance in terms of accuracy and F-measure in the generated sentiment lexicon.

scholarly journals Comparison of Instance Selection and Construction Methods with Various Classifiers

2020 ◽  
Vol 10 (11) ◽  
pp. 3933 ◽  
Author(s):  
Marcin Blachnik ◽  
Mirosław Kordos
Keyword(s):  
Classification Accuracy ◽  
Nearest Neighbors ◽  
General Purpose ◽  
Support Vector ◽  
Instance Selection ◽  
Selection Methods ◽  
Training Set ◽  
K Nearest Neighbors ◽  
Set Size ◽  
Construction Methods

Instance selection and construction methods were originally designed to improve the performance of the k-nearest neighbors classifier by increasing its speed and improving the classification accuracy. These goals were achieved by eliminating redundant and noisy samples, thus reducing the size of the training set. In this paper, the performance of instance selection methods is investigated in terms of classification accuracy and reduction of training set size. The classification accuracy of the following classifiers is evaluated: decision trees, random forest, Naive Bayes, linear model, support vector machine and k-nearest neighbors. The obtained results indicate that for the most of the classifiers compressing the training set affects prediction performance and only a small group of instance selection methods can be recommended as a general purpose preprocessing step. These are learning vector quantization based algorithms, along with the Drop2 and Drop3. Other methods are less efficient or provide low compression ratio.

scholarly journals Utilização de Modelos Computacionais Baseados em Classificadores Para Predição da Dislexia em Crianças

2021 ◽  
Author(s):  
Ronieri Nogueira de Sousa ◽  
Roney Nogueira de Sousa ◽  
Rhyan Ximenes de Brito ◽  
Janaide Nogueira de Sousa Ximenes
Keyword(s):  
Support Vector Machine ◽  
Random Forest ◽  
Radial Basis Function ◽  
Basis Function ◽  
Multilayer Perceptron ◽  
Nearest Neighbors ◽  
Support Vector ◽  
Z Score ◽  
K Nearest Neighbors ◽  
Learning Machine

A dislexia é uma das dificuldades de aprendizagem mais comum nas salas de aula. Dessa forma o estudo teve como finalidade a classificação de crianças com ou sem dislexia através da aplicação de técnicas de Inteligência Computacional (IC). Para a metodologia utilizou-se de uma base de dados pública e da aplicação das arquiteturas neurais, Multilayer Perceptron (MLP), Radial Basis Function (RBF) e Extreme Learning Machine (ELM) e dos classificadores estatísticos, Support Vector Machine (SVM), Random Forest (RF) e K-Nearest Neighbors (K-NN), assim como das técnicas k-fold, SMOTE e normalização z-score. Os resultados demonstraram que o classificador SVM obteve a melhor taxa média de acerto com 98,03% de acurácia.

scholarly journals Tropical Balls and Its Applications to K Nearest Neighbor over the Space of Phylogenetic Trees

Mathematics ◽  
2021 ◽  
Vol 9 (7) ◽  
pp. 779
Author(s):  
Ruriko Yoshida
Keyword(s):  
Supervised Learning ◽  
Phylogenetic Trees ◽  
Nearest Neighbor ◽  
Nearest Neighbors ◽  
High Dimensional ◽  
Learning Method ◽  
Dimensional Vector ◽  
K Nearest Neighbor ◽  
K Nearest Neighbors

A tropical ball is a ball defined by the tropical metric over the tropical projective torus. In this paper we show several properties of tropical balls over the tropical projective torus and also over the space of phylogenetic trees with a given set of leaf labels. Then we discuss its application to the K nearest neighbors (KNN) algorithm, a supervised learning method used to classify a high-dimensional vector into given categories by looking at a ball centered at the vector, which contains K vectors in the space.

scholarly journals Optimization of Imminent Labor Prediction Systems in Women with Threatened Preterm Labor Based on Electrohysterography

Sensors ◽  
2021 ◽  
Vol 21 (7) ◽  
pp. 2496
Author(s):  
Gema Prats-Boluda ◽  
Julio Pastor-Tronch ◽  
Javier Garcia-Casado ◽  
Rogelio Monfort-Ortíz ◽  
Alfredo Perales Marín ◽  
...  
Keyword(s):  
Preterm Labor ◽  
Cervical Length ◽  
Validation Dataset ◽  
Computationally Efficient ◽  
K Nearest Neighbors ◽  
Predictive Values ◽  
Classifier Design ◽  
Input Dataset ◽  
Threatened Preterm Labor ◽  
Learning Machine

Preterm birth is the leading cause of death in newborns and the survivors are prone to health complications. Threatened preterm labor (TPL) is the most common cause of hospitalization in the second half of pregnancy. The current methods used in clinical practice to diagnose preterm labor, the Bishop score or cervical length, have high negative predictive values but not positive ones. In this work we analyzed the performance of computationally efficient classification algorithms, based on electrohysterographic recordings (EHG), such as random forest (RF), extreme learning machine (ELM) and K-nearest neighbors (KNN) for imminent labor (<7 days) prediction in women with TPL, using the 50th or 10th–90th percentiles of temporal, spectral and nonlinear EHG parameters with and without obstetric data inputs. Two criteria were assessed for the classifier design: F1-score and sensitivity. RFF1_2 and ELMF1_2 provided the highest F1-score values in the validation dataset, (88.17 ± 8.34% and 90.2 ± 4.43%) with the 50th percentile of EHG and obstetric inputs. ELMF1_2 outperformed RFF1_2 in sensitivity, being similar to those of ELMSens (sensitivity optimization). The 10th–90th percentiles did not provide a significant improvement over the 50th percentile. KNN performance was highly sensitive to the input dataset, with a high generalization capability.

scholarly journals Privacy-Enhancing k-Nearest Neighbors Search over Mobile Social Networks

Sensors ◽  
2021 ◽  
Vol 21 (12) ◽  
pp. 3994
Author(s):  
Yuxi Li ◽  
Fucai Zhou ◽  
Yue Ge ◽  
Zifeng Xu
Keyword(s):  
Social Networks ◽  
Access Control ◽  
Location Privacy ◽  
Nearest Neighbors ◽  
Search Pattern ◽  
Broadcast Encryption ◽  
Mobile Social Networks ◽  
K Nearest Neighbors ◽  
Fine Grained ◽  
Server Architecture

Focusing on the diversified demands of location privacy in mobile social networks (MSNs), we propose a privacy-enhancing k-nearest neighbors search scheme over MSNs. First, we construct a dual-server architecture that incorporates location privacy and fine-grained access control. Under the above architecture, we design a lightweight location encryption algorithm to achieve a minimal cost to the user. We also propose a location re-encryption protocol and an encrypted location search protocol based on secure multi-party computation and homomorphic encryption mechanism, which achieve accurate and secure k-nearest friends retrieval. Moreover, to satisfy fine-grained access control requirements, we propose a dynamic friends management mechanism based on public-key broadcast encryption. It enables users to grant/revoke others’ search right without updating their friends’ keys, realizing constant-time authentication. Security analysis shows that the proposed scheme satisfies adaptive L-semantic security and revocation security under a random oracle model. In terms of performance, compared with the related works with single server architecture, the proposed scheme reduces the leakage of the location information, search pattern and the user–server communication cost. Our results show that a decentralized and end-to-end encrypted k-nearest neighbors search over MSNs is not only possible in theory, but also feasible in real-world MSNs collaboration deployment with resource-constrained mobile devices and highly iterative location update demands.

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