Efficient Training Data Reduction for SVM based Handwritten Digits Recognition

Imran, Javed; Ayyaz, Muhammad Naeem; Mehmood, Waqar

doi:10.1109/icee.2007.4287360

Cited by 5 publications

(4 citation statements)

References 6 publications

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“…In a situation where the input data set can be differentiated nonlinearly, the data set by kernel function transferred to a high-dimensional feature space to be differentiated linearly. [ 24 25 ] The most common kernel functions used in SVM include:−…”

Section: Methodsmentioning

confidence: 99%

Comparison of support vector machine based on genetic algorithm with logistic regression to diagnose obstructive sleep apnea

et al. 2018

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Background:Diagnosing of obstructive sleep apnea (OSA) is an important subject in medicine. This study aimed to compare the performance of two data mining techniques, support vector machine (SVM), and logistic regression (LR), in diagnosing OSA. The best-fit model was used as a substitute for polysomnography (PSG), which is the gold standard for diagnosing this disease.Materials and Methods:A total of 250 patients with sleep problems complaints and whose disease had been diagnosed by PSG and referred to the Sleep Disorders Research Center of Farabi Hospital, Kermanshah, between 2012 and 2015 were recruited in this study. To fit the best LR model, a model was first fitted with all variables and then compared with a model made from the significant variables using Akaike's information criterion (AIC). The SVM model and radial basis function (RBF) kernel, whose parameters had been optimized by genetic algorithm, were used to diagnose OSA.Results:Based on AIC, the best LR model obtained from this study was a model fitted with all variables. The performance of final LR model was compared with SVM model, revealing the accuracy 0.797 versus 0.729, sensitivity 0.714 versus 0.777, and specificity 0.847 vs. 0.702, respectively.Conclusion:Both models were found to have an appropriate performance. However, considering accuracy as an important criterion for comparing the performance of models in this domain, it can be argued that SVM could have a better efficiency than LR in diagnosing OSA in patients.

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Section: Methodsmentioning

confidence: 99%

Comparison of support vector machine based on genetic algorithm with logistic regression to diagnose obstructive sleep apnea

et al. 2018

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“…For each pair of samples from two different classes, Javed et al [22] plotted a sphere for each pair of samples, such that those two samples are put on two sides of the sphere diameter. If none of the other samples inside these two classes are within the sphere volume, these samples are nearest to each other from these two classes.…”

Section: Dataset Size Reductionmentioning

confidence: 99%

A New Dataset Size Reduction Approach for PCA‐Based Classification in OCR Application

Shayegan

Aghabozorgi

2014

Mathematical Problems in Engineering

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A major problem of pattern recognition systems is due to the large volume of training datasets including duplicate and similar training samples. In order to overcome this problem, some dataset size reduction and also dimensionality reduction techniques have been introduced. The algorithms presently used for dataset size reduction usually remove samples near to the centers of classes or support vector samples between different classes. However, the samples near to a class center include valuable information about the class characteristics and the support vector is important for evaluating system efficiency. This paper reports on the use of Modified Frequency Diagram technique for dataset size reduction. In this new proposed technique, a training dataset is rearranged and then sieved. The sieved training dataset along with automatic feature extraction/selection operation using Principal Component Analysis is used in an OCR application. The experimental results obtained when using the proposed system on one of the biggest handwritten Farsi/Arabic numeral standard OCR datasets, Hoda, show about 97% accuracy in the recognition rate. The recognition speed increased by 2.28 times, while the accuracy decreased only by 0.7%, when a sieved version of the dataset, which is only as half as the size of the initial training dataset, was used.

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“…For each pair of samples from two different classes, Javed et al (2007) plotted a sphere, such that those two samples are put on two sides of the sphere diameter. If none of the other samples inside these two classes are within the sphere volume, these samples are nearest to each other from these two classes.…”

Section: Related Workmentioning

confidence: 99%

A new method for Arabic/Farsi numeral data set size reduction via modified frequency diagram matching

Shayegan

Aghabozorgi²

2014

Kybernetes

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Purpose -Pattern recognition systems often have to handle problem of large volume of training data sets including duplicate and similar training samples. This problem leads to large memory requirement for saving and processing data, and the time complexity for training algorithms. The purpose of the paper is to reduce the volume of training part of a data set -in order to increase the system speed, without any significant decrease in system accuracy. Design/methodology/approach -A new technique for data set size reduction -using a version of modified frequency diagram approach -is presented. In order to reduce processing time, the proposed method compares the samples of a class to other samples in the same class, instead of comparing samples from different classes. It only removes patterns that are similar to the generated class template in each class. To achieve this aim, no feature extraction operation was carried out, in order to produce more precise assessment on the proposed data size reduction technique. Findings -The results from the experiments, and according to one of the biggest handwritten numeral standard optical character recognition (OCR) data sets, Hoda, show a 14.88 percent decrease in data set volume without significant decrease in performance. Practical implications -The proposed technique is effective for size reduction for all pictorial databases such as OCR data sets. Originality/value -State-of-the-art algorithms currently used for data set size reduction usually remove samples near to class's centers, or support vector (SV) samples between different classes. However, the samples near to a class center have valuable information about class characteristics, and they are necessary to build a system model. Also, SV s are important samples to evaluate the system efficiency. The proposed technique, unlike the other available methods, keeps both outlier samples, as well as the samples close to the class centers.

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Efficient Training Data Reduction for SVM based Handwritten Digits Recognition

Cited by 5 publications

References 6 publications

Comparison of support vector machine based on genetic algorithm with logistic regression to diagnose obstructive sleep apnea

Comparison of support vector machine based on genetic algorithm with logistic regression to diagnose obstructive sleep apnea

A New Dataset Size Reduction Approach for PCA‐Based Classification in OCR Application

A new method for Arabic/Farsi numeral data set size reduction via modified frequency diagram matching

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