scholarly journals Data Mining with Neural Networks and Support Vector Machines Using the R/rminer Tool

2010 ◽  
pp. 572-583 ◽  
Author(s):  
Paulo Cortez
Keyword(s):  
Data Mining ◽  
Neural Networks ◽  
Support Vector Machines ◽  
Support Vector ◽  
Vector Machines

scholarly journals Predicting Daily Returns of Global Stocks Indices: Neural Networks vs Support Vector Machines

2019 ◽  
pp. 1-13 ◽  
Author(s):  
Jasleen Kaur ◽  
Khushdeep Dharni
Keyword(s):  
Data Mining ◽  
Neural Networks ◽  
Support Vector Machines ◽  
Mean Square Error ◽  
Mean Absolute Error ◽  
Absolute Error ◽  
Support Vector ◽  
Mean Square ◽  
Data Mining Techniques ◽  
Vector Machines

Uniqueness in economies and stock markets has given rise to an interesting domain of exploring data mining techniques across global indices. Previously, very few studies have attempted to compare the performance of data mining techniques in diverse markets. The current study adds to the understanding regarding the variations in performance of data mining techniques across the global stock indices. We compared the performance of Neural Networks and Support Vector Machines using accuracy measures Mean Absolute Error (MAE) and R­­­­oot Mean Square Error (RMSE) across seven major stock markets. For prediction purpose, technical analysis has been employed on selected indicators based on daily values of indices spanning a period of 12 years. We created 196 data sets spanning different time periods for model building such as 1 year, 2 years, 3 years, 4 years, 6 years and 12 years for selected seven stock indices. Based on prediction models built using Neural Networks and Support Vector Machines, the findings of the study indicate there is a significant difference, both for MAE and RMSE, across the selected global indices. Also, Mean Absolute Error and Root Mean Square Error of models built using NN were greater than Mean Absolute Error and Root Mean Square Error of models built using SVM.

Predicting the Success of Ensemble Algorithms in the Banking Sector

2019 ◽  
Vol 6 (4) ◽  
pp. 12-31
Author(s):  
Özge Hüsniye Namlı Dağ
Keyword(s):  
Artificial Intelligence ◽  
Data Mining ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Support Vector Machines ◽  
Decision Tree ◽  
Banking Sector ◽  
Hybrid Methods ◽  
Support Vector ◽  
Vector Machines

The banking sector, like other service sector, improves in accordance with the customer's needs. Therefore, to know the needs of customers and to predict customer behaviors are very important for competition in the banking sector. Data mining uncovers relationships and hidden patterns in large data sets. Classification algorithms, one of the applications of data mining, is used very effectively in decision making. In this study, the c4.5 algorithm, a decision trees algorithm widely used in classification problems, is used in an integrated way with the ensemble machine learning methods in order to increase the efficiency of the algorithms. Data obtained via direct marketing campaigns from Portugal Banks was used to classify whether customers have term deposit accounts or not. Artificial Neural Networks and Support Vector Machines as Traditional Artificial Intelligence Methods and Bagging-C4.5 and Boosted-C.45 as ensemble-decision tree hybrid methods were used in classification. Bagging-C4.5 as ensemble-decision tree algorithm achieved more powerful classification success than other used algorithms. The ensemble-decision tree hybrid methods give better results than artificial neural networks and support vector machines as traditional artificial intelligence methods for this study.

scholarly journals Proposta de metodologia para a criação de etiqueta de classificação – estudo de caso: desempenho escolar

2016 ◽  
Vol 23 (1) ◽  
pp. 177-191
Author(s):  
Anderson Roges Teixeira Góes ◽  
Maria Teresinha Arns Steiner
Keyword(s):  
Data Mining ◽  
Support Vector Machines ◽  
Knowledge Discovery ◽  
Knowledge Discovery In Databases ◽  
Support Vector ◽  
Vector Machines

Resumo A qualidade na educação tem sido objeto de muita discussão, seja nas escolas e entre seus gestores, seja na mídia ou na literatura. No entanto, uma análise mais profunda na literatura parece não indicar técnicas que explorem bancos de dados com a finalidade de obter classificações para o desempenho escolar, nem tampouco há um consenso sobre o que seja “qualidade educacional”. Diante deste contexto, neste artigo, é proposta uma metodologia que se enquadra no processo KDD (Knowledge Discovery in Databases, ou seja, Descoberta de Conhecimento em Bases de Dados) para a classificação do desempenho de instituições de ensino, de forma comparativa, com base nas notas obtidas na Prova Brasil, um dos itens integrantes do Índice de Desenvolvimento da Educação Básica (IDEB) no Brasil. Para ilustrar a metodologia, esta foi aplicada às escolas públicas municipais de Araucária, PR, região metropolitana de Curitiba, PR, num total de 17, que, por ocasião da pesquisa, ofertavam Ensino Fundamental, considerando as notas obtidas pela totalidade dos alunos dos anos iniciais (1º. ao 5º. ano do ensino fundamental) e dos anos finais (6º. ao 9º. ano do ensino fundamental). Na etapa de Data Mining, principal etapa do processo KDD, foram utilizadas três técnicas de forma comparativa para o Reconhecimento de Padrões: Redes Neurais Artificiais; Support Vector Machines; e Algoritmos Genéticos. Essas técnicas apresentaram resultados satisfatórios na classificação das escolas, representados por meio de uma “Etiqueta de Classificação do Desempenho”. Por meio desta etiqueta, os gestores educacionais poderão ter melhor base para definir as medidas a serem adotadas junto a cada escola, podendo definir mais claramente as metas a serem cumpridas.

Audio-Based Condition Monitoring in Milling of the Workpiece Material With the Hardness Variation Using Support Vector Machines and Convolutional Neural Networks

2018 ◽  
Author(s):  
Achyuth Kothuru ◽  
Sai Prasad Nooka ◽  
Rui Liu
Keyword(s):  
Neural Networks ◽  
Support Vector Machines ◽  
Tool Wear ◽  
Support Vector ◽  
Workpiece Material ◽  
Artificial Intelligent ◽  
Tool Condition ◽  
Hardness Variation ◽  
Vector Machines ◽  
Audible Sound

Machining industry has been evolving towards implementation of automation into the process for higher productivity and efficiency. Although many studies have been conducted in the past to develop intelligent monitoring systems in various application scenarios of machining processes, most of them just focused on cutting tools without considering the influence due to the non-uniform hardness of workpiece material. This study develops a compact, reliable, and cost-effective intelligent Tool Condition Monitoring (TCM) model to detect the cutting tool wear in machining of the workpiece material with hardness variation. The generated audible sound signals during the machining process will be analyzed by state of the art artificial intelligent techniques, Support Vector Machines (SVMs) and Convolutional Neural Networks (CNNs), to predict the tool condition and the hardness variation of the workpiece. A four-level classification model is developed for the system to detect the tool wear condition based on the width of the flank wear land and hardness variation of the workpiece. The study also involves comparative analysis between two employed artificial intelligent techniques to evaluate the performance of models in predicting the tool wear level condition and workpiece hardness variation. The proposed intelligent models have shown a significant prediction accuracy in detecting the tool wear and from the audible sound into the proposed multi-classification wear class in the end-milling process of non-uniform hardened workpiece.

scholarly journals Impacts of multicollinearity on CAPT modalities: An heterogeneous machine learning framework for computer-assisted French phoneme pronunciation training

PLoS ONE ◽  
2021 ◽  
Vol 16 (10) ◽  
pp. e0257901
Author(s):  
Yanjing Bi ◽  
Chao Li ◽  
Yannick Benezeth ◽  
Fan Yang
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Support Vector Machines ◽  
Partial Least Square ◽  
Least Square ◽  
Support Vector ◽  
Computer Assisted ◽  
Long Distance ◽  
Relationship Analysis ◽  
Vector Machines

Phoneme pronunciations are usually considered as basic skills for learning a foreign language. Practicing the pronunciations in a computer-assisted way is helpful in a self-directed or long-distance learning environment. Recent researches indicate that machine learning is a promising method to build high-performance computer-assisted pronunciation training modalities. Many data-driven classifying models, such as support vector machines, back-propagation networks, deep neural networks and convolutional neural networks, are increasingly widely used for it. Yet, the acoustic waveforms of phoneme are essentially modulated from the base vibrations of vocal cords, and this fact somehow makes the predictors collinear, distorting the classifying models. A commonly-used solution to address this issue is to suppressing the collinearity of predictors via partial least square regressing algorithm. It allows to obtain high-quality predictor weighting results via predictor relationship analysis. However, as a linear regressor, the classifiers of this type possess very simple topology structures, constraining the universality of the regressors. For this issue, this paper presents an heterogeneous phoneme recognition framework which can further benefit the phoneme pronunciation diagnostic tasks by combining the partial least square with support vector machines. A French phoneme data set containing 4830 samples is established for the evaluation experiments. The experiments of this paper demonstrates that the new method improves the accuracy performance of the phoneme classifiers by 0.21 − 8.47% comparing to state-of-the-arts with different data training data density.

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