scholarly journals Integration of Data Mining Classification Techniques and Ensemble Learning for Predicting the Export Potential of a Company

2019 ◽  
Vol 151 ◽  
pp. 1194-1200
Author(s):  
Jesus Silva ◽  
Jenny Romero Borré ◽  
Aurora Patricia Piñeres Castillo ◽  
Ligia Castro ◽  
Noel Varela
Keyword(s):  
Data Mining ◽  
Ensemble Learning ◽  
Classification Techniques ◽  
A Company

scholarly journals Performance evaluation of different classification techniques using different datasets

2019 ◽  
Vol 9 (5) ◽  
pp. 3584
Author(s):  
Abdulkadir Özdemir ◽  
Uğur Yavuz ◽  
Fares Abdulhafidh Dael
Keyword(s):  
Data Mining ◽  
Business Intelligence ◽  
Multilayer Perceptron ◽  
Data Classification ◽  
Major Effect ◽  
Classification Techniques ◽  
The Difference ◽  
Two Parameters ◽  
Accuracy Performance

<span>Nowadays data mining become one of the technologies that paly major effect on business intelligence. However, to be able to use the data mining outcome the user should go through many process such as classified data. Classification of data is processing data and organize them in specific categorize to be use in most effective and efficient use. In data mining one technique is not applicable to be applied to all the datasets. This paper showing the difference result of applying different techniques on the same data. This paper evaluates the performance of different classification techniques using different datasets. In this study four data classification techniques have chosen. They are as follow, BayesNet, NaiveBayes, Multilayer perceptron and J48. The selected data classification techniques performance tested under two parameters, the time taken to build the model of the dataset and the percentage of accuracy to classify the dataset in the correct classification. The experiments are carried out using Weka 3.8 software. The results in the paper demonstrate that the efficiency of Multilayer Perceptron classifier in overall the best accuracy performance to classify the instances, and NaiveBayes classifiers were the worst outcome of accuracy to classifying the instance for each dataset.</span>

scholarly journals A Tutorial on Hierarchical Classification with Applications in Bioinformatics

2011 ◽  
pp. 114-140
Author(s):  
Alex Freitas ◽  
André C.P.L.F. de Carvalho
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Protein Function ◽  
Hierarchical Classification ◽  
Classification Problems ◽  
Classification Techniques ◽  
Hierarchical Relationship

In machine learning and data mining, most of the works in classification problems deal with flat classification, where each instance is classified in one of a set of possible classes and there is no hierarchical relationship between the classes. There are, however, more complex classification problems where the classes to be predicted are hierarchically related. This chapter presents a tutorial on the hierarchical classification techniques found in the literature. We also discuss how hierarchical classification techniques have been applied to the area of bioinformatics (particularly the prediction of protein function), where hierarchical classification problems are often found.

scholarly journals An Investigation of Data Mining Based Automatic Sleep Stage Classification Techniques

2019 ◽  
Vol 9 (4) ◽  
pp. 520-526
Author(s):  
Thakerng Wongsirichot ◽  
Nittida Elz ◽  
Supasit Kajkamhaeng ◽  
Wanchai Nupinit ◽  
Narongrit Sridonthong
Keyword(s):  
Data Mining ◽  
Sleep Stage ◽  
Classification Techniques ◽  
Stage Classification ◽  
Sleep Stage Classification
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