An upgraded bat algorithm for tuning extreme learning machines for data classification

Ensembles are known to reduce the risk of selecting the wrong model by aggregating all candidate models. Ensembles are known to be more accurate than single models. Accuracy has been identified as an important factor in explaining the success of ensembles. Several techniques have been proposed to improve ensemble accuracy. But, until now, no perfect one has been proposed. The focus of this research is on how to create accurate ensemble learning machine (ELM) in the context of classification to deal with supervised data, noisy data, imbalanced data, and semi-supervised data. To deal with mentioned issues, the authors propose a heterogeneous ELM ensemble. The proposed heterogeneous ensemble of ELMs (AELME) for classification has different ELM algorithms, including regularized ELM (RELM) and kernel ELM (KELM). The authors propose new diverse AdaBoost ensemble-based ELM (AELME) for binary and multiclass data classification to deal with the imbalanced data issue.

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Evolutionary parallel extreme learning machines for the data classification problem

Computers & Industrial Engineering ◽

10.1016/j.cie.2019.02.024 ◽

2019 ◽

Vol 130 ◽

pp. 237-249 ◽

Cited By ~ 5

Author(s):

Tansel Dokeroglu ◽

Ender Sevinc

Keyword(s):

Data Classification ◽

Classification Problem ◽

Extreme Learning Machines ◽

Learning Machines

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Study on optimization of rake head density of suction hopper dredger based on bat algorithm and extreme learning machine

MATEC Web of Conferences ◽

10.1051/matecconf/202030904018 ◽

2020 ◽

Vol 309 ◽

pp. 04018

Author(s):

Guangjie Hao ◽

Menghong Yu ◽

Zhen Su

Keyword(s):

Extreme Learning Machine ◽

Prediction Models ◽

Prediction Method ◽

Bat Algorithm ◽

Yangtze Estuary ◽

Extreme Learning Machines ◽

Learning Machines ◽

Density Prediction ◽

Learning Machine ◽

Selection Of

The dredging output of suction dredger mainly comes from the suction density of the rake head. Accurate prediction of suction density is of great significance to improve the dredging output of suction dredger. In order to overcome the shortcomings of low accuracy and poor real-time performance of the current inhalation density prediction methods, a bat algorithm is proposed to optimize the inhalation density prediction method of extreme learning machine. The bat algorithms for optimizing extreme learning machines prediction model is constructed based on the measured construction data of “Xinhaifeng” Yangtze Estuary, and compared with other prediction models. Finally, the bat algorithms for optimizing extreme learning machines model is used to build the output simulator of inhalation density. Compared with the actual construction, the selection of control parameters is analyzed when the output of inhalation density is the best. Experients show that bat algorithms for optimizing extreme learning machines prediction has high accuracy and good stability, and can provide scientific and effective reference for yield prediction and construction guidance.

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A novel hybrid teaching-learning-based optimization algorithm for the classification of data by using extreme learning machines

TURKISH JOURNAL OF ELECTRICAL ENGINEERING & COMPUTER SCIENCES ◽

10.3906/elk-1802-40 ◽

2019 ◽

pp. 1523-1533 ◽

Cited By ~ 6

Author(s):

ENDER SEVİNÇ ◽

TANSEL DÖKEROĞLU

Keyword(s):

Optimization Problems ◽

Classical Problem ◽

Data Classification ◽

Extreme Learning Machines ◽

Classification Problems ◽

Learning Machines ◽

Combinatorial Optimization Problems ◽

Benchmark Datasets ◽

Teaching Learning Based Optimization ◽

Teaching Learning

Data classification is the process of organizing data by relevant categories. In this way, the data can be understood and used more efficiently by scientists. Numerous studies have been proposed in the literature for the problem of data classification. However, with recently introduced metaheuristics, it has continued to be riveting to revisit this classical problem and investigate the efficiency of new techniques. Teaching-learning-based optimization (TLBO) is a recent metaheuristic that has been reported to be very effective for combinatorial optimization problems. In this study, we propose a novel hybrid TLBO algorithm with extreme learning machines (ELM) for the solution of data classification problems. The proposed algorithm (TLBO-ELM) is tested on a set of UCI benchmark datasets. The performance of TLBO-ELM is observed to be competitive for both binary and multiclass data classification problems compared with state-of-the-art algorithms.

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