Ensemble Neural Network in Classifying Handwritten Arabic Numerals

Kathirvalavakumar, T.; Palaniappan, R.

doi:10.4236/jilsa.2016.81001

Cited by 3 publications

(1 citation statement)

References 17 publications

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“…In traditional learning, only a single classifier is used to make the decision and solve a problem, whereas in ensemble learning, several classifiers are used to solve a problem [41]. The two main types of ensemble classifiers include (1) homogeneous classifiers, which use the same classifier such as RF, and (2) heterogeneous classifiers, which use a set of diverse classifiers such as LDA and DT.…”

Section: B Machine Learning 1) Single Machine Learningmentioning

confidence: 99%

Towards an Accurate Breast Cancer Classification Model based on Ensemble Learning

Hesham¹,

El-Rashidy²,

Rezk³

et al. 2022

IJACSA

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Breast cancer (BC) is considered the most common cancer among women and the major reason for the increased death rate. This condition begins in breast cells and may spread to the rest of the body tissues. The early detection and prediction of BC can help in saving a patient's life. In the last decades, machine learning (ML) has played a significant role in the development of models that can be used to detect and predict various diseases at an early stage, which can greatly increase the survival rate of patients. The importance of ML Classification is attributed to its capability to learn from previous datasets, detects patterns that are difficult to comprehend in massive datasets, predicts a categorical variable within a predefined example and provide accurate results within a short amount of time. Feature selection (FS) method was used to reduce the data dimensionality and choose the optimal feature set. In this paper, we proposed a stacking ensemble model that can differentiate between malignant and benign BC cells. A total of 25 different experiments have been conducted using several classifiers, including logistic regression (LR), decision tree (DT), linear discriminant analysis (LDA), K-nearest neighbor (KNN), naive Bayes (NB), and support vector machine (SVM). In addition to several ensembles, the classifiers included random forest (RF), bagging, AdaBoost, voting, and stacking. The results indicate that our ensemble model outperformed other state-of-the-art models in terms of accuracy (98.6%), precision (89.7%), recall, and F1 score (93.33%). The result shows that the ensemble methods with FS have a high improvement of classification accuracy rather than a single method in detecting BC accurately.

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Section: B Machine Learning 1) Single Machine Learningmentioning

confidence: 99%

Towards an Accurate Breast Cancer Classification Model based on Ensemble Learning

Hesham¹,

El-Rashidy²,

Rezk³

et al. 2022

IJACSA

View full text Add to dashboard Cite

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Novel features and a cascaded classifier based Arabic numerals recognition system

Prasad

Sanyal

2016

Multidim Syst Sign Process

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Arabic (Indian) Numeral Handwritten Recognition Using Angular Radial Transform

Mohammed¹,

Sami²

2017

djps

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In this paper, an Arabic (Indian) numeral handwritten recognition method is presented based on angular radial transform. The angular radial transform is considered as a global features extraction descriptor in order to provide distinct and rotation invariant features about the images of Arabic numeral handwritten. Also, in this, paper the performance of both angular transform and radial transform is investigated and compared. Hellinger distance measure is adopted in the classification stage to compute the distance between the test and training Arabic numeral handwritten images. The extensive experiments indicate that the proposed approach achieved a high recognition rate of 96.74% which is better of recognition rates achieved using its counterpart's angular and radial transforms which achieved 91.34% and 87.10% respectively. Also, they indicated that the performance of angular transforms is outperforms the performance of radial transform. Furthermore, observed that the proposed method is rotation invariant.

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Ensemble Neural Network in Classifying Handwritten Arabic Numerals

Cited by 3 publications

References 17 publications

Towards an Accurate Breast Cancer Classification Model based on Ensemble Learning

Towards an Accurate Breast Cancer Classification Model based on Ensemble Learning

Novel features and a cascaded classifier based Arabic numerals recognition system

Arabic (Indian) Numeral Handwritten Recognition Using Angular Radial Transform

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