scholarly journals Evaluación psicológica de profesores y alumnos mexicanos durante la pandemia de COVID-19 mediante técnicas de Machine learning

2021 ◽  
Vol 22 (4) ◽  
pp. 1-15
Author(s):  
Jesús Alejandro Navarro Acosta ◽  
Valeria Soto Mendoza ◽  
Félix Raymundo Saucedo Zendejo ◽  
José Maria Guajardo Espinoza ◽  
María Teresa Rivera Morales
Keyword(s):  
Machine Learning ◽  
Support Vector Machines ◽  
Random Forest ◽  
Support Vector ◽  
Vector Machines

En la presente obra se describe la realización de un ejercicio de validación de resultados de una prueba psicológica aplicada a maestros y alumnos en estado de aislamiento por la pandemia por COVID-19 en el estado de Coahuila, México. El objetivo de este trabajo es aplicar técnicas de machine learning para validar un instrumento que mide las emociones y los sentimientos negativos, así como el sesgo cognitivo o desviación de pensamiento sobre la educación y la pandemia en situación de aislamiento. Para el cumplimiento del objetivo se aplicó un instrumento en formato electrónico que se diseminó en el estado de Coahuila, los usuarios responden y se genera la base de datos, la cual, después de su preprocesamiento es analizada mediante la combinación de Random forest (RF) y Support Vector Machines (SVM); obteniendo como resultado la pertinencia o no de algunos de los reactivos en las pruebas, dando con esto una validez interna al instrumento. Los resultados experimentales muestran que la metodología propuesta es capaz de seleccionar las variables predictoras más relevantes. De esta manera, se obtienen resultados satisfactorios en la clasificación y predicción de diagnósticos psicológicos globales y segmentados por características de los respondientes. Por otro lado, aunque las técnicas implementadas son robustas y confiables, éstas presentan limitaciones en cuanto a la observación de los otros tipos de validez: la de constructo, la externa, entre otras; lo cual pudiera limitar su utilización. Si bien, en el campo de la psicometría existen diversas estrategias clásicas, la metodología propuesta basada en la combinación de técnicas de machine learning para el análisis y validación de este tipo de pruebas, favorece el crecimiento de opciones para mejorar los diagnósticos y en consecuencia el tratamiento de padecimientos psicológicos.

scholarly journals Prediction of Stock Prices using Random Forest and Support Vector Machines

2019 ◽  
Vol 8 (4) ◽  
pp. 473-477
Keyword(s):  
Machine Learning ◽  
Support Vector Machines ◽  
Random Forest ◽  
Stock Prices ◽  
Support Vector ◽  
Future Cost ◽  
Financial Instrument ◽  
Vector Machines ◽  
The Future ◽  
Future Value

Prediction of stock markets is the act of attempting to determine the future value of an inventory of a business or other financial instrument traded on an economic exchange.Effectively foreseeing the future cost of a stock will amplify the benefits of the financial specialist.This article suggests a model of machine learning to forecast the price of the stock market.During the way toward considering various techniques and factors that should be considered, we found that strategy, for example, random forest, support vector machines were not completely used in past structures. In this article, we will present and audit an increasingly suitable strategy for anticipating more prominent exactness stock oscillations.The primary thing we thought about was the securities exchange estimating informational index from yahoo stocks. We will audit the utilization of random forest after pre-handling the data, help the vector machine on the informational index and the outcomes it produces.The powerful stock gauge will be a superb resource for financial exchange associations and will give genuine options in contrast to the difficulties confronting the stock speculator.

scholarly journals Evaluating machine learning techniques for archaeological lithic sourcing: a case study of flint in Britain

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Tom Elliot ◽  
Robert Morse ◽  
Duane Smythe ◽  
Ashley Norris
Keyword(s):  
Machine Learning ◽  
Support Vector Machines ◽  
Random Forest ◽  
Objective Evaluation ◽  
Classification Performance ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Learning Techniques ◽  
Vector Machines

AbstractIt is 50 years since Sieveking et al. published their pioneering research in Nature on the geochemical analysis of artefacts from Neolithic flint mines in southern Britain. In the decades since, geochemical techniques to source stone artefacts have flourished globally, with a renaissance in recent years from new instrumentation, data analysis, and machine learning techniques. Despite the interest over these latter approaches, there has been variation in the quality with which these methods have been applied. Using the case study of flint artefacts and geological samples from England, we present a robust and objective evaluation of three popular techniques, Random Forest, K-Nearest-Neighbour, and Support Vector Machines, and present a pipeline for their appropriate use. When evaluated correctly, the results establish high model classification performance, with Random Forest leading with an average accuracy of 85% (measured through F1 Scores), and with Support Vector Machines following closely. The methodology developed in this paper demonstrates the potential to significantly improve on previous approaches, particularly in removing bias, and providing greater means of evaluation than previously utilised.

scholarly journals Evaluation of Light Gradient Boosted Machine Learning Technique in Large Scale Land Use and Land Cover Classification

Environments ◽  
2020 ◽  
Vol 7 (10) ◽  
pp. 84
Author(s):  
Dakota Aaron McCarty ◽  
Hyun Woo Kim ◽  
Hye Kyung Lee
Keyword(s):  
Machine Learning ◽  
Land Use ◽  
Support Vector Machines ◽  
Random Forest ◽  
Land Cover ◽  
Large Scale ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Light Gradient ◽  
Vector Machines

The ability to rapidly produce accurate land use and land cover maps regularly and consistently has been a growing initiative as they have increasingly become an important tool in the efforts to evaluate, monitor, and conserve Earth’s natural resources. Algorithms for supervised classification of satellite images constitute a necessary tool for the building of these maps and they have made it possible to establish remote sensing as the most reliable means of map generation. In this paper, we compare three machine learning techniques: Random Forest, Support Vector Machines, and Light Gradient Boosted Machine, using a 70/30 training/testing evaluation model. Our research evaluates the accuracy of Light Gradient Boosted Machine models against the more classic and trusted Random Forest and Support Vector Machines when it comes to classifying land use and land cover over large geographic areas. We found that the Light Gradient Booted model is marginally more accurate with a 0.01 and 0.059 increase in the overall accuracy compared to Support Vector and Random Forests, respectively, but also performed around 25% quicker on average.

scholarly journals Obtención de un modelo de minería de datos aplicado a la deserción universitaria del programa de Ingeniería de Sistemas de la Universidad de Cundinamarca

2020 ◽  
Vol 7 ◽  
Author(s):  
Holmes Yesid Ayala-Yaguara ◽  
Gina Maribel Valenzuela-Sabogal ◽  
Alexander Espinosa-García
Keyword(s):  
Machine Learning ◽  
Logistic Regression ◽  
Support Vector Machines ◽  
Random Forest ◽  
Nearest Neighbors ◽  
Knowledge Discovery In Databases ◽  
Support Vector ◽  
K Nearest Neighbors ◽  
Vector Machines ◽  
Feature Importance

En el presente artículo se describe la obtención de un modelo de minería de datos aplicado al problema de la deserción universitaria en el programa de Ingeniería de Sistemas de la Universidad de Cundinamarca, extensión Facatativá. El modelo se estructuró mediante la metodología de minería de datos KDD (knowledge discovery in databases) haciendo uso del lenguaje de programación Python, la librería de procesamiento de datos Pandas y de machine learning Sklearn. Para el proceso se tuvieron en cuenta problemas adicionales al proceso de minería, como, por ejemplo, la alta dimensionalidad, por lo cual se aplicaron los métodos de selección de las variables estadístico univariado, feature importance y SelectFromModel (Sklearn). En el proyecto se seleccionaron cinco técnicas de minería de datos para evaluarlas: vecinos más cercanos (K nearest neighbors, KNN), árboles de decisión (decision tree, DT), árboles aleatorios (random forest, RF), regresión logística (logistic regression, LR) y máquinas de vectores soporte (support vector machines, SVM). Respecto a la selección del modelo final se evaluaron los resultados de cada modelo en las métricas de precisión, matriz de confusión y métricas adicionales de la matriz de confusión. Por último, se ajustaron los parámetros del modelo seleccionado y se evaluó la generalización del modelo al graficar su curva de aprendizaje.

scholarly journals Research on Parallel Support Vector Machine Based on Spark Big Data Platform

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Yao Huimin
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Big Data ◽  
Support Vector Machines ◽  
Cross Validation ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Lambda Architecture ◽  
Vector Machines ◽  
Data Platform

With the development of cloud computing and distributed cluster technology, the concept of big data has been expanded and extended in terms of capacity and value, and machine learning technology has also received unprecedented attention in recent years. Traditional machine learning algorithms cannot solve the problem of effective parallelization, so a parallelization support vector machine based on Spark big data platform is proposed. Firstly, the big data platform is designed with Lambda architecture, which is divided into three layers: Batch Layer, Serving Layer, and Speed Layer. Secondly, in order to improve the training efficiency of support vector machines on large-scale data, when merging two support vector machines, the “special points” other than support vectors are considered, that is, the points where the nonsupport vectors in one subset violate the training results of the other subset, and a cross-validation merging algorithm is proposed. Then, a parallelized support vector machine based on cross-validation is proposed, and the parallelization process of the support vector machine is realized on the Spark platform. Finally, experiments on different datasets verify the effectiveness and stability of the proposed method. Experimental results show that the proposed parallelized support vector machine has outstanding performance in speed-up ratio, training time, and prediction accuracy.

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