A clustering‐based feature selection framework for handwritten Indic script classification

Chatterjee, Iman; Ghosh, Manosij; Singh, Pawan Kumar; Sarkar, Ram; Nasipuri, Mita

doi:10.1111/exsy.12459

Cited by 17 publications

(7 citation statements)

References 54 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This property motivates us to use meta-heuristic algorithms to solve the FS problem efficiently. Because of such effectiveness of meta-heuristics, it is applied on various domains like facial emotion recognition [21], image contrast enhancement [22], deluge based FS [23], wrapper-filter FS [24], FS for handwriting classification [25], digit classification [26], feature combination for handwritten numeral recognition [27] etc.…”

Section: Introductionmentioning

confidence: 99%

EHHM: Electrical Harmony Based Hybrid Meta-Heuristic for Feature Selection

et al. 2020

Self Cite

View full text Add to dashboard Cite

Selecting the most relevant features from a high dimensional dataset is always a challenging task. In this regard, the feature selection (FS) method acts as a solution to this problem mainly in the domain of data mining and machine learning. It aims at improving the performance of a learning model greatly by choosing the relevant features and ignoring the redundant ones. Besides, this also helps to achieve efficient use of space and time by the learning model under consideration. Though over the years, many metaheuristic algorithms have been proposed by the researchers to solve FS problem, still this is considered as the open research problem due to its enormous challenges. Particularly, these algorithms, at times, suffer from poor convergence because of the improper tuning of exploration and exploitation phases. Here lies the importance of the hybrid meta-heuristics which help to improve the searching capability and convergence rate of the parent algorithms. To this end, the present work introduces a new hybrid meta-heuristic FS model by combining two meta-heuristics-Harmony Search (HS) algorithm and Artificial Electric Field Algorithm (AEFA), which we have named as Electrical Harmony based Hybrid Meta-heurtistic (EHHM). The proposed hybrid meta-heuristic converges faster than its predecessors, thereby ensuring its capability to search efficiently. Usability of EHHM is examined by applying it on 18 standard UCI datasets. Moreover, to prove its supremacy, we have compared it with 10 state-of-the-art FS methods. Link to code implementation of proposed method: khalid0007/Metaheuristic-Algorithms/FS_AEFAhHS.

show abstract

Section: Introductionmentioning

confidence: 99%

EHHM: Electrical Harmony Based Hybrid Meta-Heuristic for Feature Selection

et al. 2020

Self Cite

View full text Add to dashboard Cite

show abstract

“…To outplay this issue, researchers have been working on different kinds of feature selection methods in order to obtain the optimal combination of features. Besides feature selection of medical datasets [ 32 , 33 , 34 ], researchers have utilized feature selection techniques in various domains such as handwritten script classification [ 35 , 36 ], facial emotion recognition [ 37 ], speech emotion recognition [ 38 ], and spoken language identification from audio signals [ 39 , 40 ] and have achieved notable classification accuracy improvement over the years. However, the two-phase filtering with the combination of four kinds of filter methods and classification, as well as one more phase of wrapper algorithm for the mentioned datasets have not been explored thus far.…”

Section: Literature Surveymentioning

confidence: 99%

A Tri-Stage Wrapper-Filter Feature Selection Framework for Disease Classification

Mandal

Singh

Ijaz

et al. 2021

Sensors

Self Cite

View full text Add to dashboard Cite

In machine learning and data science, feature selection is considered as a crucial step of data preprocessing. When we directly apply the raw data for classification or clustering purposes, sometimes we observe that the learning algorithms do not perform well. One possible reason for this is the presence of redundant, noisy, and non-informative features or attributes in the datasets. Hence, feature selection methods are used to identify the subset of relevant features that can maximize the model performance. Moreover, due to reduction in feature dimension, both training time and storage required by the model can be reduced as well. In this paper, we present a tri-stage wrapper-filter-based feature selection framework for the purpose of medical report-based disease detection. In the first stage, an ensemble was formed by four filter methods—Mutual Information, ReliefF, Chi Square, and Xvariance—and then each feature from the union set was assessed by three classification algorithms—support vector machine, naïve Bayes, and k-nearest neighbors—and an average accuracy was calculated. The features with higher accuracy were selected to obtain a preliminary subset of optimal features. In the second stage, Pearson correlation was used to discard highly correlated features. In these two stages, XGBoost classification algorithm was applied to obtain the most contributing features that, in turn, provide the best optimal subset. Then, in the final stage, we fed the obtained feature subset to a meta-heuristic algorithm, called whale optimization algorithm, in order to further reduce the feature set and to achieve higher accuracy. We evaluated the proposed feature selection framework on four publicly available disease datasets taken from the UCI machine learning repository, namely, arrhythmia, leukemia, DLBCL, and prostate cancer. Our obtained results confirm that the proposed method can perform better than many state-of-the-art methods and can detect important features as well. Less features ensure less medical tests for correct diagnosis, thus saving both time and cost.

show abstract

“…Just as musicians play various notes with different instruments and eventually find a perfect combination to get a harmony among the musical instruments, the candidate solutions were also fine-tuned and processed to achieve the most appropriate combination of frequencies (i.e., the final solution) which was then used to optimize the objective function. A clustering-based FS framework is proposed in [27] for handwritten Indic script classification. The clustering-based FS proceeds by applying K-means algorithm over the entire set of features after finding an optimal value for K. The features present in each cluster are then ranked and only some top percentage of features are selected from each cluster for classification.…”

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

View full text Add to dashboard Cite

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.

show abstract

A clustering‐based feature selection framework for handwritten Indic script classification

Cited by 17 publications

References 54 publications

EHHM: Electrical Harmony Based Hybrid Meta-Heuristic for Feature Selection

EHHM: Electrical Harmony Based Hybrid Meta-Heuristic for Feature Selection

A Tri-Stage Wrapper-Filter Feature Selection Framework for Disease Classification

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

Contact Info

Product

Resources

About