Analysis of Classification by Supervised and Unsupervised Learning

Shettar, Rajashekar B.; Banakar, R. M.; Nataraj, P. S. V.

doi:10.1109/iccima.2007.237

Cited by 7 publications

(3 citation statements)

References 9 publications

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“…Unsupervised learning does not rely on a labeled dataset. It is often used when the cost of labeled datasets is unacceptable (Sapkal et al, 2007). This is also a common method to reduce the dimensionality of the data.…”

Section: Data Analysis Methodsmentioning

confidence: 99%

Applications of UAS in Crop Biomass Monitoring: A Review

et al. 2021

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Biomass is an important indicator for evaluating crops. The rapid, accurate and nondestructive monitoring of biomass is the key to smart agriculture and precision agriculture. Traditional detection methods are based on destructive measurements. Although satellite remote sensing, manned airborne equipment, and vehicle-mounted equipment can nondestructively collect measurements, they are limited by low accuracy, poor flexibility, and high cost. As nondestructive remote sensing equipment with high precision, high flexibility, and low-cost, unmanned aerial systems (UAS) have been widely used to monitor crop biomass. In this review, UAS platforms and sensors, biomass indices, and data analysis methods are presented. The improvements of UAS in monitoring crop biomass in recent years are introduced, and multisensor fusion, multi-index fusion, the consideration of features not directly related to monitoring biomass, the adoption of advanced algorithms and the use of low-cost sensors are reviewed to highlight the potential for monitoring crop biomass with UAS. Considering the progress made to solve this type of problem, we also suggest some directions for future research. Furthermore, it is expected that the challenge of UAS promotion will be overcome in the future, which is conducive to the realization of smart agriculture and precision agriculture.

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Section: Data Analysis Methodsmentioning

confidence: 99%

Applications of UAS in Crop Biomass Monitoring: A Review

et al. 2021

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“…Conversely, unsupervised learning focuses on the intrinsic image-specific phenotypes and adaptively clusters unlabeled samples by exploring internal rules of data, without the need for label-oriented information. [26][27][28] Therefore, the aim of this study was to develop an innovative prediction scheme for breast cancer subtype classification (luminal subtypes [luminal A and luminal B] vs. non-luminal subtypes [human epidermal growth factor receptor 2 (HER 2) and triple negative breast cancer (TNBC)]) from dynamic contrast-enhanced (DCE) MRI based on a transfer learning strategy with unsupervised pre-training.…”

mentioning

confidence: 99%

“…Presently, most supervised learning algorithms depend on annotation information. Conversely, unsupervised learning focuses on the intrinsic image‐specific phenotypes and adaptively clusters unlabeled samples by exploring internal rules of data, without the need for label‐oriented information 26–28 …”

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confidence: 99%

Transfer Learning Strategy Based on Unsupervised Learning and Ensemble Learning for Breast Cancer Molecular Subtype Prediction Using Dynamic Contrast‐Enhanced MRI

Sun

Hou

et al. 2021

Magnetic Resonance Imaging

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Background Imaging‐driven deep learning strategies focus on training from scratch and transfer learning. However, the performance of training from scratch is often impeded by the lack of large‐scale labeled training data. Additionally, owing to the differences between source and target domains, analyzing medical image tasks satisfactorily via transfer learning based on ImageNet is difficult. Purpose To investigate two transfer learning algorithms for breast cancer molecular subtype prediction (luminal and non‐luminal) based on unsupervised pre‐training and ensemble learning: M_EL and B_EL, using malignant and benign datasets as the source domain, respectively. Study Type Retrospective. Population Eight hundred and thirty‐three female patients with histologically confirmed breast lesions (567 benign and 266 malignant cases) were selected. In the 5‐fold cross‐validation, the malignant cohort was randomly divided into 5 subsets to form a training set (80%) and a validation set (20%). Field Strength/Sequence 3.0 T, dynamic contrast‐enhanced magnetic resonance imaging (DCE‐MRI) using T1‐weighted high‐resolution isotropic volume examination. Assessment First, three datasets acquired at different times post‐contrast were preprocessed as unlabeled source domains. Second, three baseline networks corresponding to the different MRI post‐contrast phases were built, optimized by a combination of mutual information maximization between high‐ and low‐level representations and prior distribution constraints. Next, the pre‐trained networks were fine‐tuned on the labeled target domain. Finally, prediction results were integrated using weighted voting‐based ensemble learning. Statistical Tests Mean accuracy, precision, specificity, and area under receiver operating characteristic curve (AUC) were obtained with 5‐fold cross‐validation. P < 0.05 was considered to be statistically significant. Results Compared with a convolutional long short‐term memory network, pre‐trained VGG‐16, VGG‐19, and DenseNet‐121 from ImageNet, M_EL and B_EL exhibited significantly more optimized prediction performance (specificity: 90.5% and 89.9%; accuracy: 82.6% and 81.1%; precision: 91.2% and 90.9%; AUC: 0.836 and 0.823, respectively). Data Conclusion Transfer learning based on unsupervised pre‐training may facilitate automatic prediction of breast cancer molecular subtypes. Level of Evidence 3 Technical Efficacy Stage 2

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