Characterisation of environment type and difficulty for streamed data classification problems

2021 ◽  
Author(s):  
Mathys Ellis ◽  
Anna S. Bosman ◽  
Andries P. Engelbrecht
Keyword(s):  
Data Classification ◽  
Classification Problems ◽  
Environment Type

Biogeography-based optimisation for data classification problems

2017 ◽  
Vol 9 (2) ◽  
pp. 142
Author(s):  
Mohammed Alweshah ◽  
Abdelaziz I. Hammouri ◽  
Sara Tedmori
Keyword(s):  
Data Classification ◽  
Classification Problems

scholarly journals Weight Optimization in Recurrent Neural Networks with Hybrid Metaheuristic Cuckoo Search Techniques for Data Classification

2015 ◽  
Vol 2015 ◽  
pp. 1-12 ◽  
Author(s):  
Nazri Mohd Nawi ◽  
Abdullah Khan ◽  
M. Z. Rehman ◽  
Haruna Chiroma ◽  
Tutut Herawan
Keyword(s):  
Gradient Descent ◽  
Search Algorithm ◽  
Error Function ◽  
Back Propagation ◽  
Data Classification ◽  
Cuckoo Search ◽  
Recurrent Network ◽  
Training Algorithms ◽  
Local Minima ◽  
Classification Problems

Recurrent neural network (RNN) has been widely used as a tool in the data classification. This network can be educated with gradient descent back propagation. However, traditional training algorithms have some drawbacks such as slow speed of convergence being not definite to find the global minimum of the error function since gradient descent may get stuck in local minima. As a solution, nature inspired metaheuristic algorithms provide derivative-free solution to optimize complex problems. This paper proposes a new metaheuristic search algorithm called Cuckoo Search (CS) based on Cuckoo bird’s behavior to train Elman recurrent network (ERN) and back propagation Elman recurrent network (BPERN) in achieving fast convergence rate and to avoid local minima problem. The proposed CSERN and CSBPERN algorithms are compared with artificial bee colony using BP algorithm and other hybrid variants algorithms. Specifically, some selected benchmark classification problems are used. The simulation results show that the computational efficiency of ERN and BPERN training process is highly enhanced when coupled with the proposed hybrid method.

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.

scholarly journals A Comprehensive Integration Method Based on Unbalanced Data Classification Problems

2018 ◽  
Vol 1060 ◽  
pp. 012032
Author(s):  
Yu Lian ◽  
Di Zhang ◽  
Zhengkui Lin ◽  
XueHao Sun ◽  
WenLi Yu
Keyword(s):  
Integration Method ◽  
Data Classification ◽  
Unbalanced Data ◽  
Classification Problems

scholarly journals CFM-BD: A Distributed Rule Induction Algorithm for Building Compact Fuzzy Models in Big Data Classification Problems

2020 ◽  
Vol 28 (1) ◽  
pp. 163-177 ◽  
Author(s):  
Mikel Elkano ◽  
Jose Antonio Sanz ◽  
Edurne Barrenechea ◽  
Humberto Bustince ◽  
Mikel Galar
Keyword(s):  
Big Data ◽  
Data Classification ◽  
Rule Induction ◽  
Classification Problems ◽  
Fuzzy Models ◽  
Big Data Classification

scholarly journals A New Method for Solving Supervised Data Classification Problems

2014 ◽  
Vol 2014 ◽  
pp. 1-9 ◽  
Author(s):  
Parvaneh Shabanzadeh ◽  
Rubiyah Yusof
Keyword(s):  
Mining Industry ◽  
Data Classification ◽  
Classification Performance ◽  
Classification Model ◽  
Selection Algorithm ◽  
Classification Problems ◽  
Nonsmooth Nonconvex Optimization ◽  
Derivative Free ◽  
Real World Datasets ◽  
New Feature

Supervised data classification is one of the techniques used to extract nontrivial information from data. Classification is a widely used technique in various fields, including data mining, industry, medicine, science, and law. This paper considers a new algorithm for supervised data classification problems associated with the cluster analysis. The mathematical formulations for this algorithm are based on nonsmooth, nonconvex optimization. A new algorithm for solving this optimization problem is utilized. The new algorithm uses a derivative-free technique, with robustness and efficiency. To improve classification performance and efficiency in generating classification model, a new feature selection algorithm based on techniques of convex programming is suggested. Proposed methods are tested on real-world datasets. Results of numerical experiments have been presented which demonstrate the effectiveness of the proposed algorithms.

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