Online Sequential Extreme Learning Machine with Under-Sampling and Over-Sampling for Imbalanced Big Data Classification

2017 ◽  
pp. 229-239 ◽  
Author(s):  
Jie Du ◽  
Chi-Man Vong ◽  
Yajie Chang ◽  
Yang Jiao
Keyword(s):  
Big Data ◽  
Extreme Learning Machine ◽  
Data Classification ◽  
Under Sampling ◽  
Big Data Classification ◽  
Learning Machine

Elastic extreme learning machine for big data classification

2015 ◽  
Vol 149 ◽  
pp. 464-471 ◽  
Author(s):  
Junchang Xin ◽  
Zhiqiong Wang ◽  
Luxuan Qu ◽  
Guoren Wang
Keyword(s):  
Big Data ◽  
Extreme Learning Machine ◽  
Data Classification ◽  
Big Data Classification ◽  
Learning Machine

scholarly journals Imbalanced Big Data Classification using Feature Selection Under-Sampling

2019 ◽  
Vol 17 (1) ◽  
pp. 78-82
Author(s):  
Ch. Sarada ◽  
◽  
M. Sathya Devi ◽  
Keyword(s):  
Feature Selection ◽  
Big Data ◽  
Data Classification ◽  
Under Sampling ◽  
Big Data Classification

scholarly journals DSAE – Deep Stack Auto Encoder and RCBO – Rider Chaotic Biogeography Optimization Algorithm for Big Data Classification

2021 ◽  
Author(s):  
Anilkumar V. Brahmane ◽  
B Chaitanya Krishna
Keyword(s):  
Big Data ◽  
Optimization Algorithm ◽  
Data Classification ◽  
Initial Distribution ◽  
Data Set ◽  
Knn Classification ◽  
Big Data Classification ◽  
Is Integration ◽  
Learning Machine ◽  
Agricultural Banking

In today’s era Big data classification is a very crucial and equally widely arise issue is many applications. Not only engineering applications but also in social, agricultural, banking, educational and many more applications are there in science and engineering where accurate big data classification is required. We proposed a very novel and efficient methodology for big data classification using Deep stack encoder and Rider chaotic biogeography algorithms. Our proposed algorithms are the combinations of two algorithms. First one is Rider Optimization algorithm and second one is chaotic biogeography-based optimization algorithm. So, we named it as RCBO which is integration is ROA and CBBO. Our proposed system also uses the Deep stack auto encoder for the purpose of training the system which actually produced the accurate classification. The Apache spark platform is used initial distribution of the data from master node to slave nodes. Our proposed system is tested and executed on the UCI Machine learning data set which gives the excellent results while comparing with other algorithms such as KNN classification, Extreme Learning Machine Random Forest algorithms.

Cancer data classification using binary bat optimization and extreme learning machine with a novel fitness function

2019 ◽  
Vol 57 (12) ◽  
pp. 2673-2682 ◽  
Author(s):  
Kaveri Chatra ◽  
Venkatanareshbabu Kuppili ◽  
Damodar Reddy Edla ◽  
Ajeet Kumar Verma
Keyword(s):  
Extreme Learning Machine ◽  
Fitness Function ◽  
Data Classification ◽  
Cancer Data ◽  
Learning Machine

Research on complex attribute big data classification based on iterative fuzzy clustering algorithm

2021 ◽  
pp. 1-12
Author(s):  
Li Qian
Keyword(s):  
Big Data ◽  
Fuzzy Clustering ◽  
Classification Accuracy ◽  
Clustering Algorithm ◽  
Principal Component ◽  
Data Classification ◽  
Fisher Discriminant Analysis ◽  
Fuzzy Clustering Algorithm ◽  
Local Fisher Discriminant Analysis ◽  
Big Data Classification

In order to overcome the low classification accuracy of traditional methods, this paper proposes a new classification method of complex attribute big data based on iterative fuzzy clustering algorithm. Firstly, principal component analysis and kernel local Fisher discriminant analysis were used to reduce dimensionality of complex attribute big data. Then, the Bloom Filter data structure is introduced to eliminate the redundancy of the complex attribute big data after dimensionality reduction. Secondly, the redundant complex attribute big data is classified in parallel by iterative fuzzy clustering algorithm, so as to complete the complex attribute big data classification. Finally, the simulation results show that the accuracy, the normalized mutual information index and the Richter’s index of the proposed method are close to 1, the classification accuracy is high, and the RDV value is low, which indicates that the proposed method has high classification effectiveness and fast convergence speed.

Sign in / Sign up

Export Citation Format

Share Document