BMC Med Inform Decis Mak - Learning to improve medical decision making from imbalanced data without a priori cost.

Tópicos

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Resumo

CKGROUND: In a medical data set, data are commonly composed of a minority (positive or abnormal) group and a majority (negative or normal) group and the cost of misclassifying a minority sample as a majority sample is highly expensive. This is the so-called imbalanced classification problem. The traditional classification functions can be seriously affected by the skewed class distribution in the data. To deal with this problem, people often use a priori cost to adjust the learning process in the pursuit of optimal classification function. However, this priori cost is often unknown and hard to estimate in medical decision making.METHODS: In this paper, we propose a new learning method, named RankCost, to classify imbalanced medical data without using a priori cost. Instead of focusing on improving the class-prediction accuracy, RankCost is to maximize the difference between the minority class and the majority class by using a scoring function, which translates the imbalanced classification problem into a partial ranking problem. The scoring function is learned via a non-parametric boosting algorithm.RESULTS: We compare RankCost to several representative approaches on four medical data sets varying in size, imbalanced ratio, and dimension. The experimental results demonstrate that unlike the currently available methods that often perform unevenly with different priori costs, RankCost shows comparable performance in a consistent manner.CONCLUSIONS: It is a challenging task to learn an effective classification model based on imbalanced data in medical data analysis. The traditional approaches often use a priori cost to adjust the learning of the classification function. This work presents a novel approach, namely RankCost, for learning from medical imbalanced data sets without using a priori cost. The experimental results indicate that RankCost performs very well in imbalanced data classification and can be a useful method in real-world applications of medical decision making.

Resumo Limpo

ckground medic data set data common compos minor posit abnorm group major negat normal group cost misclassifi minor sampl major sampl high expens socal imbalanc classif problem tradit classif function can serious affect skew class distribut data deal problem peopl often use priori cost adjust learn process pursuit optim classif function howev priori cost often unknown hard estim medic decis makingmethod paper propos new learn method name rankcost classifi imbalanc medic data without use priori cost instead focus improv classpredict accuraci rankcost maxim differ minor class major class use score function translat imbalanc classif problem partial rank problem score function learn via nonparametr boost algorithmresult compar rankcost sever repres approach four medic data set vari size imbalanc ratio dimens experiment result demonstr unlik current avail method often perform uneven differ priori cost rankcost show compar perform consist mannerconclus challeng task learn effect classif model base imbalanc data medic data analysi tradit approach often use priori cost adjust learn classif function work present novel approach name rankcost learn medic imbalanc data set without use priori cost experiment result indic rankcost perform well imbalanc data classif can use method realworld applic medic decis make

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