【摘要】：欠抽樣是一類常見的解決非平衡數(shù)據(jù)分類的技術(shù)。傳統(tǒng)抽樣方法(如Kennard-Stone抽樣和密度保持抽樣)只考慮保持?jǐn)?shù)據(jù)分布。已有欠抽樣方法側(cè)重抽取分類邊界附近的樣本,這樣抽取的樣本可能改變數(shù)據(jù)的原始分布特征,從而影響分類效果。提出數(shù)據(jù)冗余度的概念,即如果一個(gè)多數(shù)類樣本處于多數(shù)類的密集區(qū)且距離分類邊界或少數(shù)類樣本較遠(yuǎn),則樣本冗余度較高。去冗余抽樣(Redundancy-removed Sampling,RRS)采用傳統(tǒng)抽樣規(guī)則去掉多數(shù)類中冗余度相對(duì)較高的樣本。這樣的樣本子集盡量包含對(duì)分類最有幫助的樣本和保持原始數(shù)據(jù)分布,且兩類樣本數(shù)量相對(duì)均衡。實(shí)驗(yàn)結(jié)果表明,經(jīng)RRS抽樣的分類結(jié)果的總體精度高于其他抽樣方法,尤其在分類精度較低的數(shù)據(jù)集上。同時(shí),少數(shù)類樣本的判別精度也有所提高。
[Abstract]:Undersampling is a common technique to solve the problem of unbalance data classification. Traditional sampling methods (such as Kennard-Stone sampling and density preserving sampling) only consider preserving data distribution. Existing under-sampling methods focus on sampling samples near the classification boundary, which may change the original distribution characteristics of the data, thus affecting the classification effect. The concept of data redundancy is proposed, that is, if a sample of most classes is located in a dense area of most classes and is far from the classification boundary or a few samples, the redundancy of the sample is higher. De-redundancy sampling (Redundancy-removed Sampling,RRS) adopts traditional sampling rules to remove samples with relatively high redundancy in most classes. Such a subset of samples contains the most helpful samples for classification and maintains the distribution of raw data, and the two types of samples are relatively balanced in number. The experimental results show that the overall accuracy of the classification results with RRS sampling is higher than that of other sampling methods, especially on the data sets with low classification accuracy. At the same time, the discriminant accuracy of a few kinds of samples is improved.
【作者單位】： 太原師范學(xué)院科研處;太原師范學(xué)院計(jì)算機(jī)系;
【基金】：山西省青年科學(xué)基金(201601D202040)
【分類號(hào)】：O212.2
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本文編號(hào)：2343495