Recognition of different datasets using PCA, LDA, and various classifiers

Panahi, Nazila; Shayesteh, Mahrokh G.; Mihandoost, Sara; Varghahan, Behrooz Zali

doi:10.1109/icaict.2011.6110912

Cited by 17 publications

(11 citation statements)

References 9 publications

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“…It also gives us the chance to select the useful components by separating them from useless components using the component variances. This selection process is an important step to take because having these less-ranked components will affect the learning curves and the classification process [12]. As a result, the study had approximately thirteen new principal components.…”

Section: E Principal Component Analysis (Pca)mentioning

confidence: 99%

“…The study had to use the PCA to identify underlying hidden fusions and use the orthogonal transformation to transform the MF correlated features into a new set of values of linearly uncorrelated attributes. By implementing the PCA, the study was able to exploit and reduce the MF dimensions to a new set of principal components that could be used in representation, evaluation, and, more importantly, classification [10], [12].…”

Section: E Principal Component Analysis (Pca)mentioning

confidence: 99%

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Demographic Group Classification of Smart Device Users

Alharbi

Thornton

2015

2015 IEEE 14th International Conference on Machine Learning and Applications (ICMLA)

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Interacting with smart devices is a common experience and is becoming an integral part of daily life for many people. Modern smart devices are equipped with a large variety of environmental and user input sensors. We hypothesize that a fusion of smart device sensor data can provide biometric data that allows for classification of user demographics such as age, gender, and native language. A smart device is instrumented with sensor data collection software and with user demographic classification software. An experiment is devised where data is collected for a sample group of users. The data is analyzed, and two classification algorithms are implemented based on the fusion of the different sensors. The classification methods are based upon decision tree and principle component analysis. The results of the experiment indicate that high accuracy is achieved for user demographic classification. Finally, we further discuss the applications and limitations of the study's approach.

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Section: E Principal Component Analysis (Pca)mentioning

confidence: 99%

Section: E Principal Component Analysis (Pca)mentioning

confidence: 99%

Demographic Group Classification of Smart Device Users

Alharbi

Thornton

2015

2015 IEEE 14th International Conference on Machine Learning and Applications (ICMLA)

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“…On the hyperplane, the distances among vectors of the same class are minimal, and the distance of different class centers is maximum. In this way, LDA offers more class separability and draws a decision region between the given classes [48].…”

Section: ) Classifiermentioning

confidence: 99%

Identification of Epileptic Seizures by Characterizing Instantaneous Energy Behavior of EEG

et al. 2019

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Automatic seizure detection has been often treated as a classification problem that aims at determining the label of electroencephalogram (EEG) signals by computer science, as the EEG monitoring is a helpful adjunct to the diagnosis of epilepsy. In most existing work, the traditional signal energy of the EEG has been applied for classification, since the energy pattern of epileptic seizures differs from that of non-seizures. Although they are effective, the accuracy either heavily depends on additional information besides energy or is limited by the shortcoming of energy-based features. To address this issue, the proposed approach achieves the classification based on the instantaneous energy of the EEG signals instead. The proposed approach first measures the instantaneous energy related to changes in the EEG signals. Then, energy behavior over time is characterized by instantaneous energy-based features from different aspects. Finally, the classification is carried out on the features to produce output labels. By processing instantaneous energy, the information of energy evolution is involved. As such, the accuracy is improved without bringing in extra information besides energy, or complicated transformation. In multi-class problems, the proposed approach has obtained promising results for identifying the ictal EEG, which indicates the tremendous potential of the proposed approach for epileptic seizure detection. INDEX TERMSSeizure identification, electroencephalogram (EEG) signal, instantaneous energy, energy behavior.

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“…It finds the project axes to project the data samples of the dissimilar classes to be far from each other and to close the data samples of the same class. Hence, the LDA generates a linear combination of the data samples, which achieves the largest differences between the classes [23]. At the same time, the SVM determine decision boundaries according to the decision planes, which separate the set of features of the different classes.…”

Section: Performance Evaluation Comparative Studymentioning

confidence: 99%

Comparative study of multiclass classification methods on light microscopic images for hepatic schistosomiasis fibrosis diagnosis

Ashour

Hawas

Guo

2018

Health Inf Sci Syst

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Hepatic schistosomiasis is a prolonged disease resulting mainly from the solvable egg antigen of schistosomiasis infection due to the host's granulomatous cell-mediated immune. Irreversible fibrosis results from the progress of the schistosomal hepatopathy. Sensitive diagnosis of this disease is based on the investigation of the microscopy images, liver tissues, and egg identification. Early diagnosis of schistosomiasis at its initial infection stage is vital to avoid egg-induced irreparable pathological reactions. Typically, there are several classification approaches that can be used for liver fibrosis staging. However, it is unclear which approaches can achieve high accuracy for analyzing and intelligently classifying the liver microscopic images. Consequently, this work aims to study the performance of the different machine learning classifiers for accurate fibrosis level staging of granuloma, namely cellular, fibrocellular and fibrotic granulomas as well as the normal samples. The classifiers include a multi-layer perceptron neural network, a decision tree, discriminant analysis, support vector machine (SVM), nearest neighbor, and the ensemble of classifiers. The statistical features of the microscopic images are extracted from the different fibrosis levels of granuloma, namely cellular, fibrocellular and fibrotic granulomas as well as the normal samples. The results established that the maximum achieved classification accuracies of value 90% were achieved using the subspace discriminant ensemble, the quadratic SVM, the linear SVM, or the linear discriminant classifiers. However, the linear discriminant classifier can be considered the superior classifier as it realized the best area under the curve of value 0.96 during the classification of the cellular granuloma as well as the fibro-cellular granuloma fibrosis levels.

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Recognition of different datasets using PCA, LDA, and various classifiers

Cited by 17 publications

References 9 publications

Demographic Group Classification of Smart Device Users

Demographic Group Classification of Smart Device Users

Identification of Epileptic Seizures by Characterizing Instantaneous Energy Behavior of EEG

Comparative study of multiclass classification methods on light microscopic images for hepatic schistosomiasis fibrosis diagnosis

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