基于逻辑判别式(LD,Logistic Discrimination),提出一种叫做LDRC(LD based Rare-class Classification)方法用于提升LD在稀有类问题中的泛化性能.为了充分考虑稀有类的特性,构建了一种新目标函数RPM(Recall and Precision based M etri...基于逻辑判别式(LD,Logistic Discrimination),提出一种叫做LDRC(LD based Rare-class Classification)方法用于提升LD在稀有类问题中的泛化性能.为了充分考虑稀有类的特性,构建了一种新目标函数RPM(Recall and Precision based M etric),其同时考虑正类和负类的召回率以及正类的精度,其中正类和负类的召回率用于保障模型在评估指标召回率以及g-mean(正类和分类的召回率的几何平均数)上具有较高的泛化能力,正类的召回率和精度用于保障了模型具有较高的准确率以及fmeasure值(基于正类召回率与精度的指标).LDRC使用RPM作为目标函数监督参数学习过程,以保障LDRC具有较高的整体泛化能力.UCI数据集上的实验结果表明,与传统的逻辑判别、基于过采样和基于欠采样的逻辑判别相比,LDRC模型在评价指标召回率、g-mean和f-measure上都表现出明显优势.展开更多
On May 23, 2017, the US Food and Drug Administration （FDA） approved a treatment for cancer patients with positive microsatellite instability-high （MSI-H） markers or mismatch repair deficient （dMMR） markers. This...On May 23, 2017, the US Food and Drug Administration （FDA） approved a treatment for cancer patients with positive microsatellite instability-high （MSI-H） markers or mismatch repair deficient （dMMR） markers. This approach is the first approved tumor treatment using a common biomarker rather than specified tumor locations in the body. FDA previously approved Keytruda for treatment of several types of malignancies, such as metastatic melanoma, metastatic non-small-cell lung cancer, recurrent or metastatic head and neck cancer, refractory Hodgkin lymphoma, and urothelial carcinoma, all of which carry positive programmed death-l/ programmed death-ligand 1 biomarkers. Therefore, indications of Keytruda significantly expanded. Several types of malignancies are disclosed by MSI-H status due to dMMR and characterized by increased neoantlgen load, which elicits intense host immune response in tumor microenvironment, including portions of colorectal and gastric carcinomas. Currently, biomarker-based patient selection remains a challenge. Pathologists play important roles in evaluating histology and biomarker results and establishing detection methods. Taking gastric cancer as an example, its molecular classification is built on genome abnormalities, but it lacks acceptable clinical characteristics. Pathologists are expected to act as ＂genetic interpreters＂ or ＂genetic translators＂ and build a link between molecular subtypes with tumor histological features. Subsequently, by using their findings, oncologists will carry out targeted therapy based on molecular classification.展开更多
Various indices are used for assessing vegetation and soil properties in satellite remote sensing applications. Some indices,such as normalized difference vegetation index(NDVI) and normalized difference water index(N...Various indices are used for assessing vegetation and soil properties in satellite remote sensing applications. Some indices,such as normalized difference vegetation index(NDVI) and normalized difference water index(NDWI), are capable of simply differentiating crop vitality and water stress. Nowadays, remote sensing capabilities with high spectral, spatial and temporal resolution are available to analyse classification problems in precision agriculture. Many challenges in precision agriculture can be addressed by supervised classification, such as crop type classification, disease and stress(e.g., grass, water and nitrogen) monitoring. Instead of performing classification based on designated indices, this paper explores direct classification using different bands information as features. Land cover classification by using the recently launched Sentinel-2A image is adopted as a case study to validate our method. Four approaches of featured band selection are compared to classify five classes(crop, tree, soil, water and road) with the support vector machines(SVMs)algorithm, where the first approach utilizes traditional empirical indices as features and the latter three approaches adopt specific bands(red, near infrared and short wave infrared) related to indices, specific bands after ranking by mutual information(MI), and full bands of on-board sensors as features, respectively. It is shown that a better classification performance can be achieved by directly using the selected bands after MI ranking compared with the one using empirical indices and specific bands related to indices, while the use of all 13 bands can marginally improve the classification accuracy than MI based one. Therefore, it is recommended that this approach can be applied for specific Sentinel-2A image classification problems in precision agriculture.展开更多
文摘基于逻辑判别式(LD,Logistic Discrimination),提出一种叫做LDRC(LD based Rare-class Classification)方法用于提升LD在稀有类问题中的泛化性能.为了充分考虑稀有类的特性,构建了一种新目标函数RPM(Recall and Precision based M etric),其同时考虑正类和负类的召回率以及正类的精度,其中正类和负类的召回率用于保障模型在评估指标召回率以及g-mean(正类和分类的召回率的几何平均数)上具有较高的泛化能力,正类的召回率和精度用于保障了模型具有较高的准确率以及fmeasure值(基于正类召回率与精度的指标).LDRC使用RPM作为目标函数监督参数学习过程,以保障LDRC具有较高的整体泛化能力.UCI数据集上的实验结果表明,与传统的逻辑判别、基于过采样和基于欠采样的逻辑判别相比,LDRC模型在评价指标召回率、g-mean和f-measure上都表现出明显优势.
文摘On May 23, 2017, the US Food and Drug Administration （FDA） approved a treatment for cancer patients with positive microsatellite instability-high （MSI-H） markers or mismatch repair deficient （dMMR） markers. This approach is the first approved tumor treatment using a common biomarker rather than specified tumor locations in the body. FDA previously approved Keytruda for treatment of several types of malignancies, such as metastatic melanoma, metastatic non-small-cell lung cancer, recurrent or metastatic head and neck cancer, refractory Hodgkin lymphoma, and urothelial carcinoma, all of which carry positive programmed death-l/ programmed death-ligand 1 biomarkers. Therefore, indications of Keytruda significantly expanded. Several types of malignancies are disclosed by MSI-H status due to dMMR and characterized by increased neoantlgen load, which elicits intense host immune response in tumor microenvironment, including portions of colorectal and gastric carcinomas. Currently, biomarker-based patient selection remains a challenge. Pathologists play important roles in evaluating histology and biomarker results and establishing detection methods. Taking gastric cancer as an example, its molecular classification is built on genome abnormalities, but it lacks acceptable clinical characteristics. Pathologists are expected to act as ＂genetic interpreters＂ or ＂genetic translators＂ and build a link between molecular subtypes with tumor histological features. Subsequently, by using their findings, oncologists will carry out targeted therapy based on molecular classification.
基金supported by Science and Technology Facilities Council (STFC) under Newton fund (No. ST/N006852/1)Chinese Scholarship Council (CSC) for supporting his study in the UK
文摘Various indices are used for assessing vegetation and soil properties in satellite remote sensing applications. Some indices,such as normalized difference vegetation index(NDVI) and normalized difference water index(NDWI), are capable of simply differentiating crop vitality and water stress. Nowadays, remote sensing capabilities with high spectral, spatial and temporal resolution are available to analyse classification problems in precision agriculture. Many challenges in precision agriculture can be addressed by supervised classification, such as crop type classification, disease and stress(e.g., grass, water and nitrogen) monitoring. Instead of performing classification based on designated indices, this paper explores direct classification using different bands information as features. Land cover classification by using the recently launched Sentinel-2A image is adopted as a case study to validate our method. Four approaches of featured band selection are compared to classify five classes(crop, tree, soil, water and road) with the support vector machines(SVMs)algorithm, where the first approach utilizes traditional empirical indices as features and the latter three approaches adopt specific bands(red, near infrared and short wave infrared) related to indices, specific bands after ranking by mutual information(MI), and full bands of on-board sensors as features, respectively. It is shown that a better classification performance can be achieved by directly using the selected bands after MI ranking compared with the one using empirical indices and specific bands related to indices, while the use of all 13 bands can marginally improve the classification accuracy than MI based one. Therefore, it is recommended that this approach can be applied for specific Sentinel-2A image classification problems in precision agriculture.
