M-HMOGA: A New Multi-Objective Feature Selection Algorithm for Handwritten Numeral Classification

Guha, Ritam; Ghosh, Manosij; Singh, Pawan Kumar; Sarkar, Ram; Nasipuri, Mita

doi:10.1515/jisys-2019-0064

Cited by 17 publications

(8 citation statements)

References 32 publications

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“…The proposed model known as Wrapper-Filter ACOFS (WFACOFS) used both wrapper and filter methods to evaluate its candidate solutions, thereby reducing the time requirement of the overall model (wrapper methods require more time to evaluate candidates than filter). Guha et al proposed another level of improvement over HMOGA in [36] where they added memory to the existing technique to store the best candidate solutions generated over the iterations which are eventually lost in the process. The model was then applied on handwritten numeral classification datasets.…”

Section: Related Studymentioning

confidence: 99%

“…Therefore, from the literature review, it can be noticed that application of FS in handwritten digit or word recognition is quite well addressed. For example, in [36,37], the authors have applied FS for solving the problem of handwritten Devanagari digit recognition, whereas the work described in [38] implements FS for handwritten Bangla word classification. However, to the best of our knowledge, FS has rarely been used for handwritten ASC problem.…”

Section: Related Studymentioning

confidence: 99%

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A Hybrid Swarm and Gravitation-based feature selection algorithm for handwritten Indic script classification problem

Guha

Ghosh

Singh

et al. 2021

Complex Intell. Syst.

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In any multi-script environment, handwritten script classification is an unavoidable pre-requisite before the document images are fed to their respective Optical Character Recognition (OCR) engines. Over the years, this complex pattern classification problem has been solved by researchers proposing various feature vectors mostly having large dimensions, thereby increasing the computation complexity of the whole classification model. Feature Selection (FS) can serve as an intermediate step to reduce the size of the feature vectors by restricting them only to the essential and relevant features. In the present work, we have addressed this issue by introducing a new FS algorithm, called Hybrid Swarm and Gravitation-based FS (HSGFS). This algorithm has been applied over three feature vectors introduced in the literature recently—Distance-Hough Transform (DHT), Histogram of Oriented Gradients (HOG), and Modified log-Gabor (MLG) filter Transform. Three state-of-the-art classifiers, namely, Multi-Layer Perceptron (MLP), K-Nearest Neighbour (KNN), and Support Vector Machine (SVM), are used to evaluate the optimal subset of features generated by the proposed FS model. Handwritten datasets at block, text line, and word level, consisting of officially recognized 12 Indic scripts, are prepared for experimentation. An average improvement in the range of 2–5% is achieved in the classification accuracy by utilizing only about 75–80% of the original feature vectors on all three datasets. The proposed method also shows better performance when compared to some popularly used FS models. The codes used for implementing HSGFS can be found in the following Github link: https://github.com/Ritam-Guha/HSGFS.

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Section: Related Studymentioning

confidence: 99%

Section: Related Studymentioning

confidence: 99%

A Hybrid Swarm and Gravitation-based feature selection algorithm for handwritten Indic script classification problem

Guha

Ghosh

Singh

et al. 2021

Complex Intell. Syst.

Self Cite

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“…Apart from the above-mentioned works, a wide range of recent metaheuristics have been employed to solve feature selection problems [56]- [62] as well as other problems [63]- [67]. Some recently proposed hybrid algorithms applied to FS are hybrid BALO [68], hybrid Grey Wolf Optimizer [69], [70], hybrid ACO [18], hybrid GA [71], [72] etc. Thus, the increasing popularity of FS and the application of metaheuristic algorithms in this domain is clearly visible.…”

Section: Related Workmentioning

confidence: 99%

Embedded chaotic whale survival algorithm for filter–wrapper feature selection

et al. 2020

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Classification accuracy provided by a machine learning model depends a lot on the feature set used in the learning process. Feature Selection (FS) is an important and challenging pre-processing technique which helps to identify only the relevant features from a dataset thereby reducing the feature dimension as well as improving the classification accuracy at the same time. The binary version of Whale Optimization Algorithm (WOA) is a popular FS technique which is inspired from the foraging behavior of humpback whales. In this paper, an embedded version of WOA called Embedded Chaotic Whale Survival Algorithm (ECWSA) has been proposed which uses its wrapper process to achieve high classification accuracy and a filter approach to further refine the selected subset with low computation cost. Chaos has been introduced in the ECWSA to guide selection of the type of movement followed by the whales while searching for prey. A fitness-dependent death mechanism has also been introduced in the system of whales which is inspired from the real-life scenario in which whales die if they are unable to catch their prey. The proposed method has been evaluated on 18 well-known UCI datasets and compared with its predecessors as well as some other popular FS methods.

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“…They tested the proposed algorithm over UCI datasets which shows the superiority of DGA over some well-established contemporary metaheuristic algorithms. As an improvement over HMOGA, Guha et al proposed a memory-oriented HMOGA named M-HMOGA in [25] which uses a memory and stores best population of GA across multiple generations. Abualigah and Hanandeh applied adaptive GA to perform information retrieval using the vector space model in [2].…”

Section: Related Workmentioning

confidence: 99%

Binary Genetic Swarm Optimization: A Combination of GA and PSO for Feature Selection

Ghosh

Guha

Alam

et al. 2019

Journal of Intelligent Systems

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Abstract Feature selection (FS) is a technique which helps to find the most optimal feature subset to develop an efficient pattern recognition model under consideration. The use of genetic algorithm (GA) and particle swarm optimization (PSO) in the field of FS is profound. In this paper, we propose an insightful way to perform FS by amassing information from the candidate solutions produced by GA and PSO. Our aim is to combine the exploitation ability of GA with the exploration capacity of PSO. We name this new model as binary genetic swarm optimization (BGSO). The proposed method initially lets GA and PSO to run independently. To extract sufficient information from the feature subsets obtained by those, BGSO combines their results by an algorithm called average weighted combination method to produce an intermediate solution. Thereafter, a local search called sequential one-point flipping is applied to refine the intermediate solution further in order to generate the final solution. BGSO is applied on 20 popular UCI datasets. The results were obtained by two classifiers, namely, k nearest neighbors (KNN) and multi-layer perceptron (MLP). The overall results and comparisons show that the proposed method outperforms the constituent algorithms in 16 and 14 datasets using KNN and MLP, respectively, whereas among the constituent algorithms, GA is able to achieve the best classification accuracy for 2 and 7 datasets and PSO achieves best accuracy for 2 and 4 datasets, respectively, for the same set of classifiers. This proves the applicability and usefulness of the method in the domain of FS.

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M-HMOGA: A New Multi-Objective Feature Selection Algorithm for Handwritten Numeral Classification

Cited by 17 publications

References 32 publications

A Hybrid Swarm and Gravitation-based feature selection algorithm for handwritten Indic script classification problem

A Hybrid Swarm and Gravitation-based feature selection algorithm for handwritten Indic script classification problem

Embedded chaotic whale survival algorithm for filter–wrapper feature selection

Binary Genetic Swarm Optimization: A Combination of GA and PSO for Feature Selection

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