Multi Hive Artificial Bee Colony Programming for high dimensional symbolic regression with feature selection

Arslan, Sibel; Öztürk, Celal

doi:10.1016/j.asoc.2019.03.014

Cited by 47 publications

(11 citation statements)

References 36 publications

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“…An increase in the accuracy and a lesser computational complexity was attained by integrating a multi-objective optimization algorithm with a sample reduction technique using ABC [71]. [72] presented a variant of ABC called a multi-hive artificial bee colony for high-dimensional symbolic regression with feature selection. Grover and Chawla used an intelligent strategy to improve the ABC algorithm [73].…”

Section: It Takes Into Cognizant the Interaction Of Featuresmentioning

confidence: 99%

A systematic review of emerging feature selection optimization methods for optimal text classification: the present state and prospective opportunities

Omolara

Abiodun

Alawida

et al. 2021

Neural Comput & Applic

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Specialized data preparation techniques, ranging from data cleaning, outlier detection, missing value imputation, feature selection (FS), amongst others, are procedures required to get the most out of data and, consequently, get the optimal performance of predictive models for classification tasks. FS is a vital and indispensable technique that enables the model to perform faster, eliminate noisy data, remove redundancy, reduce overfitting, improve precision and increase generalization on testing data. While conventional FS techniques have been leveraged for classification tasks in the past few decades, they fail to optimally reduce the high dimensionality of the feature space of texts, thus breeding inefficient predictive models. Emerging technologies such as the metaheuristics and hyper-heuristics optimization methods provide a new paradigm for FS due to their efficiency in improving the accuracy of classification, computational demands, storage, as well as functioning seamlessly in solving complex optimization problems with less time. However, little details are known on best practices for case-to-case usage of emerging FS methods. The literature continues to be engulfed with clear and unclear findings in leveraging effective methods, which, if not performed accurately, alters precision, real-world-use feasibility, and the predictive model's overall performance. This paper reviews the present state of FS with respect to metaheuristics and hyper-heuristic methods. Through a systematic literature review of over 200 articles, we set out the most recent findings and trends to enlighten analysts, practitioners and researchers in the field of data analytics seeking clarity in understanding and implementing effective FS optimization methods for improved text classification tasks.

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Section: It Takes Into Cognizant the Interaction Of Featuresmentioning

confidence: 99%

A systematic review of emerging feature selection optimization methods for optimal text classification: the present state and prospective opportunities

Omolara

Abiodun

Alawida

et al. 2021

Neural Comput & Applic

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“…In [76] a Multi Hive ABC Programming is developed to select the final feature set in high dimensional datasets. This approach utilized the ability of an automatic programming algorithm to remove irrelevant and redundant features.…”

Section: Related Workmentioning

confidence: 99%

A novel community detection based genetic algorithm for feature selection

2021

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The feature selection is an essential data preprocessing stage in data mining. The core principle of feature selection seems to be to pick a subset of possible features by excluding features with almost no predictive information as well as highly associated redundant features. In the past several years, a variety of meta-heuristic methods were introduced to eliminate redundant and irrelevant features as much as possible from high-dimensional datasets. Among the main disadvantages of present meta-heuristic based approaches is that they are often neglecting the correlation between a set of selected features. In this article, for the purpose of feature selection, the authors propose a genetic algorithm based on community detection, which functions in three steps. The feature similarities are calculated in the first step. The features are classified by community detection algorithms into clusters throughout the second step. In the third step, features are picked by a genetic algorithm with a new community-based repair operation. Nine benchmark classification problems were analyzed in terms of the performance of the presented approach. Also, the authors have compared the efficiency of the proposed approach with the findings from four available algorithms for feature selection. Comparing the performance of the proposed method with three new feature selection methods based on PSO, ACO, and ABC algorithms on three classifiers showed that the accuracy of the proposed method is on average 0.52% higher than the PSO, 1.20% higher than ACO, and 1.57 higher than the ABC algorithm.

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“…ABCP is a high-level automatic programming method based on ABC algorithm [4,31,32]. The steps of the ABCP are similar to the ABC algorithm.…”

Section: Artificial Bee Colony Programmingmentioning

confidence: 99%

Artificial Bee Colony Programming Descriptor for Multi-Class Texture Classification

Arslan

Öztürk

2019

Applied Sciences

Self Cite

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Texture classification is one of the machine learning methods that attempts to classify textures by evaluating samples. Extracting related features from the samples is necessary to successfully classify textures. It is a very difficult task to extract successful models in the texture classification problem. The Artificial Bee Colony (ABC) algorithm is one of the most popular evolutionary algorithms inspired by the search behavior of honey bees. Artificial Bee Colony Programming (ABCP) is a recently introduced high-level automatic programming method for a Symbolic Regression (SR) problem based on the ABC algorithm. ABCP has applied in several fields to solve different problems up to date. In this paper, the Artificial Bee Colony Programming Descriptor (ABCP-Descriptor) is proposed to classify multi-class textures. The models of the descriptor are obtained with windows sliding on the textures. Each sample in the texture dataset is defined instance. For the classification of each texture, only two random selected instances are used in the training phase. The performance of the descriptor is compared standard Local Binary Pattern (LBP) and Genetic Programming-Descriptor (GP-descriptor) in two commonly used texture datasets. When the results are evaluated, the proposed method is found to be a useful method in image processing and has good performance compared to LBP and GP-descriptor.

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Multi Hive Artificial Bee Colony Programming for high dimensional symbolic regression with feature selection

Cited by 47 publications

References 36 publications

A systematic review of emerging feature selection optimization methods for optimal text classification: the present state and prospective opportunities

A systematic review of emerging feature selection optimization methods for optimal text classification: the present state and prospective opportunities

A novel community detection based genetic algorithm for feature selection

Artificial Bee Colony Programming Descriptor for Multi-Class Texture Classification

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