解决异常值和过拟合问题的增强鲁棒单变量分类方法

Q4 Computer Science
F. Okwonu, N. Ahad, Hashibah Hamid, N. Muda, Olimjon Shukurovich Sharipov
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引用次数: 2

摘要

一些经典的单变量分类器在数据被污染时,其鲁棒性会受到影响。当数据集没有受到相当大的样本量的污染时,过拟合是另一个问题。分类模型的性能很容易受到异常值问题的影响,构造的模型容易出现过拟合的情况。以往的研究通常使用贝叶斯分类器(BC)和预测分类器(PC)来解决两组单变量分类问题。不幸的是,对于大量的样本量和未受污染的数据,当使用最优精确分类概率(OPEC)作为评估基准时,BC方法会过拟合。同时,对于小样本量,BC和PC方法极易受到异常值的影响。为了克服这两个问题,我们提出了两种方法:智能单变量分类器(SUC)和混合分类器。后者是SUC和BC方法的结合,被称为智能单变量贝叶斯分类器(SUBC)。以欧佩克为基准,对新分类方法的性能进行了评估,并与传统的BC和PC方法进行了比较。为了验证这些分类方法的性能,将准确分类概率(PEC)与OPEC值进行了比较。结果表明,基于实际数据集,本文提出的方法优于传统的BC和PC方法。数值结果也表明,该方法可以很好地解决过拟合问题。结果进一步表明,这两种方法对异常值具有鲁棒性。因此,当从业者面临过拟合和数据污染问题时,这些新方法是有帮助的。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Enhanced Robust Univariate Classification Methods for Solving Outliers and Overfitting Problems
The robustness of some classical univariate classifiers is hampered if the data are contaminated. Overfitting is another hiccup when the data sets are uncontaminated with a considerable sample size. The performance of the classification models can be easily biased by the outliers’ problems, of which the constructed model tends to be overfitted. Previous studies often used the Bayes Classifier (BC) and the Predictive Classifier (PC) to address two groups of univariate classification problems. Unfortunately for substantial large sample sizes and uncontaminated data, the BC method overfits when the Optimal Probability of Exact Classification (OPEC) is used as an evaluation benchmark. Meanwhile, for small sample sizes, the BC and PC methods are extremely susceptible to outliers. To overcome these two problems, we proposed two methods: the Smart Univariate Classifier (SUC) and the hybrid classifier. The latter is a combination of the SUC and the BC methods, known as the Smart Univariate Bayes Classifier (SUBC). The performance of the new classification methods was evaluated and compared with the conventional BC and PC methods using the OPEC as a benchmark value. To validate the performance of these classification methods, the Probability of Exact Classification (PEC) was compared with the OPEC value. The results showed that the proposed methods outperformed the conventional BC and PC methods based on the real data sets applied. Numerical results also revealed that the SUC method could solve the overfitting problem. The results further indicated that the two proposed methods were robust against outliers. Therefore, these new methods are helpful when practitioners are confronted with overfitting and data contamination problems.
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来源期刊
CiteScore
0.70
自引率
0.00%
发文量
95
期刊介绍: IJICT is a refereed journal in the field of information and communication technology (ICT), providing an international forum for professionals, engineers and researchers. IJICT reports the new paradigms in this emerging field of technology and envisions the future developments in the frontier areas. The journal addresses issues for the vertical and horizontal applications in this area. Topics covered include: -Information theory/coding- Information/IT/network security, standards, applications- Internet/web based systems/products- Data mining/warehousing- Network planning, design, administration- Sensor/ad hoc networks- Human-computer intelligent interaction, AI- Computational linguistics, digital speech- Distributed/cooperative media- Interactive communication media/content- Social interaction, mobile communications- Signal representation/processing, image processing- Virtual reality, cyber law, e-governance- Microprocessor interfacing, hardware design- Control of industrial processes, ERP/CRM/SCM
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