Retracted: Naive Credal Classifier for Uncertain Data Classification

2013 ◽  
pp. 121-132
Author(s):  
S. Sai Satyanarayana Reddy ◽  
G. V. Suresh ◽  
T. Raghunadha Reddy ◽  
B. Vishnu Vardhan
Keyword(s):  
Uncertain Data ◽  
Data Classification

scholarly journals Multi Labeled Imbalanced Data Classification Based on Advanced Min-Max Machine Learning

2019 ◽  
Vol 9 (1) ◽  
pp. 1776-1778
Keyword(s):  
Machine Learning ◽  
Uncertain Data ◽  
Imbalanced Data ◽  
Data Classification ◽  
Data Sets ◽  
Imbalanced Data Classification ◽  
Different Types ◽  
Measured System ◽  
Traditional Approaches

Some true applications, for example, content arrangement and sub-cell confinement of protein successions, include multi-mark grouping with imbalanced information. Different types of traditional approaches are introduced to describe the relation of hubristic and undertaking formations, classification of different attributes with imbalanced for different uncertain data sets. Here this addresses the issues by utilizing the min-max particular system. The min-max measured system can break down a multi-mark issue into a progression of little two-class sub-issues, which would then be able to be consolidated by two straightforward standards. Additionally present a few decay procedures to improve the presentation of min-max particular systems. Trial results on sub-cellular restriction demonstrate that our strategy has preferable speculation execution over customary SVMs in settling the multi-name and imbalanced information issues. In addition, it is additionally a lot quicker than customary SVMs

scholarly journals Rough set based ensemble learning algorithm for agricultural data classification

Filomat ◽  
2018 ◽  
Vol 32 (5) ◽  
pp. 1917-1930 ◽  
Author(s):  
Lei Shi ◽  
Qiguo Duan ◽  
Juanjuan Zhang ◽  
Lei Xi ◽  
Hongbo Qiao ◽  
...  
Keyword(s):  
Ensemble Learning ◽  
Rough Set ◽  
Rough Set Theory ◽  
Learning Algorithm ◽  
Uncertain Data ◽  
Data Classification ◽  
Research Area ◽  
Experimental Comparison ◽  
Classification Problems ◽  
Ensemble Learning Algorithm

Agricultural data classification attracts more and more attention in the research area of intelligent agriculture. As a kind of important machine learning methods, ensemble learning uses multiple base classifiers to deal with classification problems. The rough set theory is a powerful mathematical approach to process unclear and uncertain data. In this paper, a rough set based ensemble learning algorithm is proposed to classify the agricultural data effectively and efficiently. An experimental comparison of different algorithms is conducted on four agricultural datasets. The results of experiment indicate that the proposed algorithm improves performance obviously.

Object-to-group probabilistic distance measure for uncertain data classification

2017 ◽  
Vol 230 ◽  
pp. 143-151 ◽  
Author(s):  
Behnam Tavakkol ◽  
Myong Kee Jeong ◽  
Susan L. Albin
Keyword(s):  
Distance Measure ◽  
Uncertain Data ◽  
Data Classification ◽  
Probabilistic Distance
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