Machine Learning-Based Data Classification Techniques in Healthcare Using Massive Online Analysis Framework

2021 ◽  
pp. 213-236
Author(s):  
B. Ida Seraphim ◽  
E. Poovammal
Keyword(s):  
Machine Learning ◽  
Data Classification ◽  
Analysis Framework ◽  
Online Analysis ◽  
Classification Techniques

scholarly journals Selecting critical features for data classification based on machine learning methods

2020 ◽  
Vol 7 (1) ◽  
Author(s):  
Rung-Ching Chen ◽  
Christine Dewi ◽  
Su-Wen Huang ◽  
Rezzy Eko Caraka
Keyword(s):  
Machine Learning ◽  
Data Classification ◽  
Learning Methods ◽  
Machine Learning Methods

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 study secure multi authentication based data classification model in cloud based system

2020 ◽  
Vol 9 (3) ◽  
pp. 240
Author(s):  
Sakshi Kaushal ◽  
Bala Buksh
Keyword(s):  
Machine Learning ◽  
Cloud Computing ◽  
Data Classification ◽  
Classification Model ◽  
Sensitive Data ◽  
Learning Technique ◽  
Mathematical Algorithms ◽  
Encryption Algorithms ◽  
Cloud Applications ◽  
Technology Resources

Cloud computing is the most popular term among enterprises and news. The concepts come true because of fast internet bandwidth and advanced cooperation technology. Resources on the cloud can be accessed through internet without self built infrastructure. Cloud computing is effectively manage the security in the cloud applications. Data classification is a machine learning technique used to predict the class of the unclassified data. Data mining uses different tools to know the unknown, valid patterns and relationships in the dataset. These tools are mathematical algorithms, statistical models and Machine Learning (ML) algorithms. In this paper author uses improved Bayesian technique to classify the data and encrypt the sensitive data using hybrid stagnography. The encrypted and non encrypted sensitive data is sent to cloud environment and evaluate the parameters with different encryption algorithms.

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.

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