A Linear-RBF Multikernel SVM to Classify Big Text Corpora

The aim of this study was to explore the possibility of applying Fourier transform infrared(FTIR) spectroscopy as a medical diagnostic tool based on a neural network classifier for detecting and classifying cholangiocarcinoma. A total of 51 cases of bile duct tissues were obtained and later characterized by FTIR spectroscopy prior to pathological diagnosis. The criteria for classification included 30 parameters for each FTIR spectra, including peak position(P), intensity(I) and full width at half-maximum(FWHM), were measured, calculated and subsequently compared against the normal and cancer groups. The FTIR spectra were classified by the radial basis function(RBF) network model. For establishing the RBF, 23 cases were used to train the RBF classifier, and 28 cases were applied to validate the model. Using the RFB model, nine parameters were observed to be pronouncedly different between cancerous and normal tissue, including I and P 1040 . In the RBF training classification, the accuracy, sensitivity, and specificity of diagnosis were 82.6%, 80.0%, and 84.6%, respectively. While validating the classification, the accuracy, sensitivity, and specificity of diagnosis were 78.6%, 75.0%, and 81.2%, respectively. The results suggest that FTIR spectroscopy combined with neural network classifier could be applied as a medical diagnostic tool in cholangiocarcinoma diagnosis.

show abstract

“…the ith neuron of the hidden layer, and ∑ is the neuron of the output layer, y i (i=1, ···, m) is the ith output of RBF NN [14] . was defined as …”

Section: Discrimination Analysis Methodsmentioning

confidence: 99%

Detection of cholangiocarcinoma with Fourier transform infrared spectroscopy and radial basis function neural network classification

Cui

Ling

et al. 2016

Chem. Res. Chin. Univ.

View full text Add to dashboard Cite

show abstract

“…The state-of-the-art in text classification usually applies machine learning techniques such as SVM [24]. However, SVM is not suitable for large datasets or text corpora, because the training complexity of SVM is highly dependent on the input size [25]. Comparing the processing time, SVM takes longer time than NB and k-NN during classifier training but is faster than k-NN during classification.…”

Section: ) Naïve Bayesmentioning

confidence: 99%

“…It is a linear learner in its basic form, but can be configured to learn polynomial classifiers, radial basic function (RBF) networks and three-layer sigmoid neural nets by applying appropriate kernel function. In a classification procedure carried out on big text corpora, [25] concluded that RBF and Sigmoid kernels need higher time to build model and requires additional parameters as compared to linear SVM. It is difficult to determine its parameterization with imbalanced data.…”

Section: ) Naïve Bayesmentioning

confidence: 99%

Stemming Text-based Web Page Classification using Machine Learning Algorithms: A Comparison

Razali¹,

Mohd²,

Azan³

et al. 2020

IJACSA

View full text Add to dashboard Cite

The research aim is to determine the effect of word-stemming in web pages classification using different machine learning classifiers, namely Naïve Bayes (NB), k-Nearest Neighbour (k-NN), Support Vector Machine (SVM) and Multilayer Perceptron (MP). Each classifiers' performance is evaluated in term of accuracy and processing time. This research uses BBC dataset that has five predefined categories. The result demonstrates that classifiers' performance is better without word stemming, whereby all classifiers show higher classification accuracy, with the highest accuracy produced by NB and SVM at 97% for F1 score, while NB takes shorter training time than SVM. With word stemming, the effect on training and classification time is negligible, except on Multilayer Perceptron in which word stemming has effectively reduced the training time.

show abstract

“…In general, discrimination accuracy of a Gaussian kernel is high compared to another kernel function. However, if dimension of learning data is large, a linear kernel is comparatively better than the nonlinear Gaussian kernel, and can obtain the lowest cost [21]. Therefore, we selected the linear kernel to SVM.…”

Section: Learningmentioning

confidence: 99%

Japanese Janken Recognition by Support Vector Machine Based on Electromyogram of Wrist

Hiraoka¹,

Ito

et al. 2017

ECTI-CIT

View full text Add to dashboard Cite

We propose a method which can discriminate hand motions in this paper. We measure an electromyogram (EMG) of wrist by using 8 dry type sensors. We focus on four motions, such as “Rock-Scissors-Paper” and “Neutral”. “Neutral” is a state that does not do anything. In the proposed method, we apply fast Fourier transformation (FFT) to measured EMG data, and then remove a hum noise. Next, we combine values of sensors based on a Gaussian function. In this Gaussian function, variance and mean are 0.2 and 0, respectively. We then apply normalization by linear transformation to the values. Subsequently, we resize the values into the range from -1 to 1. Finally, a support vector machine (SVM) conducts learning and discrimination to classify them. We conducted experiments with seven subjects. Average of discrimination accuracy was 89.8%. In the previous method, the discrimination accuracy was 77.1%. Therefore, the proposed method is better in accuracy than the previous method. In future work, we will conduct an experiment which discriminates Japanese Janken of a subject who is not learned.

show abstract

A Linear-RBF Multikernel SVM to Classify Big Text Corpora

Cited by 28 publications

References 41 publications

Detection of cholangiocarcinoma with Fourier transform infrared spectroscopy and radial basis function neural network classification

Detection of cholangiocarcinoma with Fourier transform infrared spectroscopy and radial basis function neural network classification

Stemming Text-based Web Page Classification using Machine Learning Algorithms: A Comparison

Japanese Janken Recognition by Support Vector Machine Based on Electromyogram of Wrist

Contact Info

Product

Resources

About