Cost-Sensitive学习的一个新课题

Cost-Sensitive学习注重分类过程中的各种代价,特别是误分类代价和属性代价。在Cost-Sensitive学习中还有一种代价对分类的总代价有较大影响,这种代价可称为分类延时代价,即延误分类而造成的代价。包含分类延时代价的Cost-Sensitive学习是Cost-Sensitive学习中一个新的课题,这个新课题的目标是使分类过程中的误分类代价、检查代价及分类延时代价之和达到最小。给出包含一种简单的分类延时代价的Cost-Sensitive学习,提出一个既"摊薄"延时代价又减少浪费检查代价的检查策略。

Dual Channel Pricing of Large Fresh Chain Stores Considering Distribution Costs under Split Quotation

After large fresh food chain stores have opened online channels,distribution costs are a key factor affecting consumers'online buying behavior,which affects dual-channel pricing.This paper studies the dual-channel pricing strategy of large fresh food chain stores on the premise of dividing the quotation,considering the consumer's acceptance of online channels and the sensitivity to distribution costs.The research found that the optimal pricing of online channels is lower than that of retail channels.The optimal pricing of online channels is positively correlated with the acceptance of online channels,and negatively correlated with the sensitivity of consumer distribution costs.Moreover,after retailers have opened online channels,the market scale has expanded compared with traditional retail channels.Finally,numerical experiments are used to analyze the influence of various influencing factors on retailers'decision-making.

Influences of misprediction costs on solar flare prediction

The mispredictive costs of flaring and non-flaring samples are different for different applications of solar flare prediction.Hence,solar flare prediction is considered a cost sensitive problem.A cost sensitive solar flare prediction model is built by modifying the basic decision tree algorithm.Inconsistency rate with the exhaustive search strategy is used to determine the optimal combination of magnetic field parameters in an active region.These selected parameters are applied as the inputs of the solar flare prediction model.The performance of the cost sensitive solar flare prediction model is evaluated for the different thresholds of solar flares.It is found that more flaring samples are correctly predicted and more non-flaring samples are wrongly predicted with the increase of the cost for wrongly predicting flaring samples as non-flaring samples,and the larger cost of wrongly predicting flaring samples as non-flaring samples is required for the higher threshold of solar flares.This can be considered as the guide line for choosing proper cost to meet the requirements in different applications.

Dynamic Treatment Strategy of Chinese Medicinefor Metastatic Colorectal Cancer Based on Machine Learning Algorithm

Objective:To establish the dynamic treatment strategy of Chinese medicine(CM)for metastatic colorectal cancer(mCRC)by machine learning algorithm,in order to provide a reference for the selection of CM treatment strategies for mCRC.Methods:From the outpatient cases of mCRC in the Department of Oncology at Xiyuan Hospital,China Academy of Chinese Medical Sciences,197 cases that met the inclusion criteria were screened.According to different CM intervention strategies,the patients were divided into 3 groups:CM treatment alone,equal emphasis on Chinese and Western medicine treatment(CM combined with local treatment of tumors,oral chemotherapy,or targeted drugs),and CM assisted Western medicine treatment(CM combined with intravenous regimen of Western medicine).The survival time of patients undergoing CM intervention was taken as the final evaluation index.Factors affecting the choice of CM intervention scheme were screened as decision variables.The dynamic CM intervention and treatment strategy for mCRC was explored based on the cost-sensitive classification learning algorithm for survival(CSCLSurv).Patients'survival was estimated using the Kaplan-Meier method,and the survival time of patients who received the model-recommended treatment plan were compared with those who received actual treatment plan.Results:Using the survival time of patients undergoing CM intervention as the evaluation index,a dynamic CM intervention therapy strategy for mCRC was established based on CSCLSurv.Different CM intervention strategies for mCRC can be selected according to dynamic decision variables,such as gender,age,Eastern Cooperative Oncology Group score,tumor site,metastatic site,genotyping,and the stage of Western medicine treatment at the patient's first visit.The median survival time of patients who received the model-recommended treatment plan was 35 months,while those who receive the actual treatment plan was 26.0 months(P=0.06).Conclusions:The dynamic treatment strategy of CM,based on CSCLSurv for mCRC,plays a certain role in providing clinical hints in CM.It can be further improved in future prospective studies with larger sample sizes.

Selecting decision trees for power system security assessment

Power systems transport an increasing amount of electricity,and in the future,involve more distributed renewables and dynamic interactions of the equipment.The system response to disturbances must be secure and predictable to avoid power blackouts.The system response can be simulated in the time domain.However,this dynamic security assessment(DSA)is not computationally tractable in real-time.Particularly promising is to train decision trees(DTs)from machine learning as interpretable classifiers to predict whether the systemwide responses to disturbances are secure.In most research,selecting the best DT model focuses on predictive accuracy.However,it is insufficient to focus solely on predictive accuracy.Missed alarms and false alarms have drastically different costs,and as security assessment is a critical task,interpretability is crucial for operators.In this work,the multiple objectives of interpretability,varying costs,and accuracies are considered for DT model selection.We propose a rigorous workflow to select the best classifier.In addition,we present two graphical approaches for visual inspection to illustrate the selection sensitivity to probability and impacts of disturbances.We propose cost curves to inspect selection combining all three objectives for the first time.Case studies on the IEEE 68 bus system and the French system show that the proposed approach allows for better DT-selections,with an 80%increase in interpretability,5%reduction in expected operating cost,while making almost zero accuracy compromises.The proposed approach scales well with larger systems and can be used for models beyond DTs.Hence,this work provides insights into criteria for model selection in a promising application for methods from artificial intelligence(AI).