一种基于XGboost的异常检测算法

为了提高异常检测的准确性和高效性,提出了基于xgboost的异常检测算法.首先对异常检测当前遇到的挑战进行分析,指出缺少样本和模型泛化是异常检测中的难点.在此基础上设计了异常注入算法,利用3sigma原则对数据集进行扩充;然后设计特征提取器,针对正常数据和异常数据的特点设计相关特征;最后选择xgboost模型对时序数据...     

基于机器学习的自动负载均衡方法研究及应用

负载均衡是提升LTE网络容量能力的重要优化手段,由于LTE小区用户在时间上的波动性,常规的人工优化手段很难满足网络优化的需求。本文提出了一种基于机器学习XGBoost用户数预测的自动均衡方法和系统,通过XGBoost对LTE小区未来15 min的用户数量进行精准预测,并根据预测结果进行转移用户数计算,从而实现均衡门限的实时动态调整。该方法在网络应用中取得了良好的效果。     

Machine Learning and Artificial Neural Network for Predicting Heart Failure Risk

Heart failure is now widely spread throughout the world. Heart disease affects approximately 48% of the population. It is too expensive and also difficult to cure the disease. This research paper represents machine learning models to predict heart failure. The fundamental concept is to compare the correctness of various Machine Learning (ML) algorithms and boost algorithms to improve models’ accuracy for prediction. Some supervised algorithms like K-Nearest Neighbor (KNN), Support Vector Machine (SVM), Decision Trees (DT), Random Forest (RF), Logistic Regression (LR) are considered to achieve the best results. Some boosting algorithms like Extreme Gradient Boosting (XGBoost) and CatBoost are also used to improve the prediction using Artificial Neural Networks (ANN). This research also focuses on data visualization to identify patterns, trends, and outliers in a massive data set. Python and Scikit-learns are used for ML. Tensor Flow and Keras, along with Python, are used for ANN model training. The DT and RF algorithms achieved the highest accuracy of 95% among the classifiers. Meanwhile, KNN obtained a second height accuracy of 93.33%. XGBoost had a gratified accuracy of 91.67%, SVM, CATBoost, and ANN had an accuracy of 90%, and LR had 88.33% accuracy.     

Toward Quantitative Models in Safety Assessment: A Case Study to Show Impact of Dose–Response Inference on hERG Inhibition Models

Due to challenges with historical data and the diversity of assay formats, in silico models for safety-related endpoints are often based on discretized data instead of the data on a natural continuous scale. Models for discretized endpoints have limitations in usage and interpretation that can impact compound design. Here, we present a consistent data inference approach, exemplified on two data sets of Ether-à-go-go-Related Gene (hERG) K+ inhibition data, for dose–response and screening experiments that are generally applicable for in vitro assays. hERG inhibition has been associated with severe cardiac effects and is one of the more prominent safety targets assessed in drug development, using a wide array of in vitro and in silico screening methods. In this study, the IC₅₀ for hERG inhibition is estimated from diverse historical proprietary data. The IC₅₀ derived from a two-point proprietary screening data set demonstrated high correlation (R = 0.98, MAE = 0.08) with IC_50s derived from six-point dose–response curves. Similar IC₅₀ estimation accuracy was obtained on a public thallium flux assay data set (R = 0.90, MAE = 0.2). The IC₅₀ data were used to develop a robust quantitative model. The model’s MAE (0.47) and R² (0.46) were on par with literature statistics and approached assay reproducibility. Using a continuous model has high value for pharmaceutical projects, as it enables rank ordering of compounds and evaluation of compounds against project-specific inhibition thresholds. This data inference approach can be widely applicable to assays with quantitative readouts and has the potential to impact experimental design and improve model performance, interpretation, and acceptance across many standard safety endpoints.