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Improving Accuracy and Coverage of Data Mining Systems that are Built from Noisy Datasets: A New Model

机译:从嘈杂的数据集构建的数据挖掘系统的准确性和覆盖范围的提高:一种新模型

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摘要

Problem statement: Noise within datasets has to be dealt with under most circumstances. This noise includes misclassified data or information as well as missing data or information. Simple human error is considered as misclassification. These errors will decrease the accuracy of the data mining system so it will not be likely to be used. The objective was to propose an effective algorithm to deal with noise which is represented by missing data in datasets. Approach: A model for improving the accuracy and coverage of data mining systems was proposed and the algorithm of this model was constructed. The algorithm was dealing with missing values in datasets. It splits the original dataset into two new datasets; one contains tuples that have no missing values and the other one contains tuples that have missing values. The proposed algorithm was applied to each of the two new datasets. It finds the reduct of each of them and then it merges the new reducts into one new dataset which will be ready for training. Results: The results showed interesting as it increases the accuracy and coverage of the tested dataset compared to the traditional models. Conclusion: The proposed algorithm performs effectively and generates better results than the previous ones.
机译:问题陈述:在大多数情况下,必须处理数据集中的噪声。这种噪声包括分类错误的数据或信息以及丢失的数据或信息。简单的人为错误被认为是错误分类。这些错误将降低数据挖掘系统的准确性,因此将不太可能使用它。目的是提出一种有效的算法来处理由数据集中的缺失数据所代表的噪声。方法:提出了一种提高数据挖掘系统准确性和覆盖范围的模型,并构建了该模型的算法。该算法正在处理数据集中的缺失值。它将原始数据集分为两个新数据集;一个包含没有缺失值的元组,另一个包含没有缺失值的元组。所提出的算法已应用于两个新数据集。它找到每个样本的归约,然后将新的归约合并到一个新的数据集中,准备进行训练。结果:结果显示出有趣的结果,因为与传统模型相比,它提高了测试数据集的准确性和覆盖范围。结论:所提出的算法比以前的算法有效并且产生了更好的结果。

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