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دسته بندی:
داده های بزرگ - big data
سال انتشار:
2020
عنوان انگلیسی مقاله:
Data imbalance in classification: Experimental evaluation
ترجمه فارسی عنوان مقاله:
عدم تعادل داده ها در طبقه بندی: ارزیابی تجربی
منبع:
Sciencedirect - Elsevier - Information Sciences, 513 (2020) 429-441: doi:10:1016/j:ins:2019:11:004
نویسنده:
Fadi Thabtah a , 1 , ∗, Suhel Hammoud b , Firuz Kamalov c , Amanda Gonsalves a
چکیده انگلیسی:
The advent of Big Data has ushered a new era of scientific breakthroughs. One of the com- mon issues that affects raw data is class imbalance problem which refers to imbalanced distribution of values of the response variable. This issue is present in fraud detection, network intrusion detection, medical diagnostics, and a number of other fields where neg- atively labeled instances significantly outnumber positively labeled instances. Modern ma- chine learning techniques struggle to deal with imbalanced data by focusing on minimizing the error rate for the majority class while ignoring the minority class. The goal of our pa- per is demonstrate the effects of class imbalance on classification models. Concretely, we study the impact of varying class imbalance ratios on classifier accuracy. By highlighting the precise nature of the relationship between the degree of class imbalance and the cor- responding effects on classifier performance we hope to help researchers to better tackle the problem. To this end, we carry out extensive experiments using 10-fold cross validation on a large number of datasets. In particular, we determine that the relationship between the class imbalance ratio and the accuracy is convex.
Keywords: Classification | Class imbalance | Data analysis | Machine learning | Statistical analysis | Supervised learning
قیمت: رایگان
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