Imbalanced Multi-Class Classification of Hyperspectral Image Based on Smote and Deep Rotation Forest

In this study, the performances of random forest (RF), rotation forest (RoF), and canonical correlation forest (CCF) algorithms were compared and analyzed for classification of hyperspectral imagery. For this purpose, the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) Indian Pine (IP), the Reflective Optics System Imaging Spectrometer University of Pavia, and the AVIRIS Kennedy Space Center (KSC) data sets were used as main data sources. In addition to the confusion matrix–derived accuracy measures (overall accuracy, kappa coefficient, F-scores), the performances of the algorithms were analyzed in detail considering three diversity measures (Q statistics, correlations, and interrater agreements) and a kappa-error diagram. Results showed that the highest classification accuracies (87% for IP, 94% for PU, and 93% for KSC data sets) were achieved with the use of CCF algorithm, and improvements in classification accuracy were statistically significant compared to RF and RoF. Based on the diversity measures and the kappa-error diagram, individual learners in the CCF ensemble were found to be more diverse and accurate.

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A Single-trial Multi-class Classification of Various Motor Imagery Tasks for EEG-based Brain-computer Interface Communication

IEEJ Transactions on Sensors and Micromachines ◽

10.1541/ieejsmas.135.239 ◽

2015 ◽

Vol 135 (7) ◽

pp. 239-245

Author(s):

Tadanobu Misawa ◽

Jumpei Matsuda ◽

Shigeki Hirobayashi

Keyword(s):

Motor Imagery ◽

Brain Computer Interface ◽

Computer Interface ◽

Single Trial ◽

Multi Class Classification

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A Study on Multi Class Classification from Breast Cancer Images using Ensemble Network and Transfer Learning

Recent Patents on Engineering ◽

10.2174/1872212114999201109205421 ◽

2020 ◽

Vol 14 ◽

Author(s):

Lahari Tipirneni ◽

Rizwan Patan

Keyword(s):

Breast Cancer ◽

Neural Network ◽

Convolutional Neural Network ◽

Binary Classification ◽

Disease Diagnosis ◽

Feature Descriptors ◽

Histopathological Images ◽

Viable Approach ◽

Multi Class Classification

Abstract:: Millions of deaths all over the world are caused by breast cancer every year. It has become the most common type of cancer in women. Early detection will help in better prognosis and increases the chance of survival. Automating the classification using Computer-Aided Diagnosis (CAD) systems can make the diagnosis less prone to errors. Multi class classification and Binary classification of breast cancer is a challenging problem. Convolutional neural network architectures extract specific feature descriptors from images, which cannot represent different types of breast cancer. This leads to false positives in classification, which is undesirable in disease diagnosis. The current paper presents an ensemble Convolutional neural network for multi class classification and Binary classification of breast cancer. The feature descriptors from each network are combined to produce the final classification. In this paper, histopathological images are taken from publicly available BreakHis dataset and classified between 8 classes. The proposed ensemble model can perform better when compared to the methods proposed in the literature. The results showed that the proposed model could be a viable approach for breast cancer classification.

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