Feature Weighting of Support Vector Machines Based on Derivative Saliency Analysis and Its Application to Financial Data Mining

2012 ◽  
Vol 4 (1) ◽  
pp. 199-206 ◽  
Author(s):  
Chuanhe Shen ◽  
Xiangrong Wang ◽  
Di Yu
Keyword(s):  
Data Mining ◽  
Support Vector Machines ◽  
Feature Weighting ◽  
Financial Data ◽  
Support Vector ◽  
Vector Machines ◽  
Saliency Analysis

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.

CREDIT SCORING MODELS WITH AUC MAXIMIZATION BASED ON WEIGHTED SVM

2009 ◽  
Vol 08 (04) ◽  
pp. 677-696 ◽  
Author(s):  
LIGANG ZHOU ◽  
KIN KEUNG LAI ◽  
JEROME YEN
Keyword(s):  
Support Vector Machines ◽  
Quantitative Methods ◽  
Credit Scoring ◽  
Feature Weighting ◽  
Computational Time ◽  
Support Vector ◽  
Operating Characteristics ◽  
Auc Maximization ◽  
Vector Machines ◽  
Rbf Kernel

Credit scoring models are very important tools for financial institutions to make credit granting decisions. In the last few decades, many quantitative methods have been used for the development of credit scoring models with focus on maximizing classification accuracy. This paper proposes the credit scoring models with the area under receiver operating characteristics curve (AUC) maximization based on the new emerged support vector machines (SVM) techniques. Three main SVM models with different features weighted strategies are discussed. The weighted SVM credit scoring models are tested using 10-fold cross validation with two real world data sets and the experimental results are compared with other six traditional methods including linear regression, logistic regression, k nearest neighbor, decision tree, and neural network. Results demonstrate that weighted 2-norm SVM with radial basis function (RBF) kernel function and t-test feature weighting strategy has the overall better performance with very narrow margin than other SVM models. However, it also consumes more computational time. In considering the balance of performance and time, least squares support vector machines (LSSVM) with RBF kernel maybe a better choice for large scale credit scoring applications.

scholarly journals Prediction of course completion by students of a university in Brazil

Psico-USF ◽  
2018 ◽  
Vol 23 (3) ◽  
pp. 425-436
Author(s):  
Alessandra Turini Bolsoni-Silva ◽  
Rommel Melgaço Barbosa ◽  
Alessandra Salina Brandão ◽  
Sonia Regina Loureiro
Keyword(s):  
Data Mining ◽  
Academic Performance ◽  
Support Vector Machines ◽  
Young People ◽  
University Students ◽  
Course Completion ◽  
Simple Approach ◽  
Support Vector ◽  
Reliability Sensitivity ◽  
Vector Machines

Abstract The conclusion of the undergraduate course by university students in the time predicted by the curriculum is desirable for young people and for society. The aim was to verify the reliability, sensitivity and specificity of a broad set of predictors for academic performance of university students, who completed the undergraduate course within the time predicted by the curricula, through data mining methodology, provided by the Support Vector Machines algorithm. A simple approach is proposed for the prediction of course completion by students in a university in Brazil. The dataset has 170 students who finished the course and 117 who did not finish. With the proposed methodology, it was possible to predict the course completion by students with an accuracy of 79.5% when using the 19 original variables. An accuracy of 75% was found using only 05 variables: Course, year of the course, gender, initial and final academic performance.

Data Mining in Identifying Carbonate Litho-Facies from Well Logs Based fromExtreme Learning and Support Vector Machines

2010 ◽  
Author(s):  
Emad A. El-Sebakhy ◽  
Ognian Asparouhov ◽  
Abdulazeez Abdulraheem ◽  
Abdul-Aziz Al-Majed ◽  
Donghui Wu ◽  
...  
Keyword(s):  
Data Mining ◽  
Support Vector Machines ◽  
Well Logs ◽  
Support Vector ◽  
Vector Machines
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