Evaluating the Importance of each Feature in Classification task

机译：评估分类任务中每个要素的重要性

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In Machine Learning and statistics attribute/feature selection is used in predictive model construction. This help the Machine in interpreting the features easily by discovering good insight and improves efficiency in predictive modeling. The objective of our research is to improve the classification accuracy by knowing the most important feature from any given dataset. In this research, we used two techniques namely Data partition and K Fold, in evaluating the importance of each feature from the randomly generated dataset with 5399 instances and 20 attributes. In Data partitioning, the attribute with lowest accuracy is filtered out. Where as in K Fold cross validation, attributes with biggest error is removed from the original dataset. In our experiments, the evaluation parameters considered are Recall. Precision and F-Measure. Finally the accuracy rate of both the techniques are compared. The finding in our research stats that K Fold approach achieves better accuracy of 97.03% than Data partitioning(96.11%) in estimating the importance of features in classification.

机译：在机器学习和统计中，属性/功能选择用于预测模型的构建。通过发现良好的洞察力，这有助于机器轻松地解释功能，并提高预测建模的效率。我们研究的目的是通过了解任何给定数据集的最重要特征来提高分类准确性。在这项研究中，我们使用了数据分区和K折叠这两种技术，从具有5399个实例和20个属性的随机生成的数据集中评估每个功能的重要性。在数据分区中，精度最低的属性被滤除。就像在K Fold交叉验证中一样，从原始数据集中删除具有最大错误的属性。在我们的实验中，考虑的评估参数是召回率。精度和F量度。最后，比较了两种技术的准确率。在我们的研究统计中发现，在估计特征在分类中的重要性时，K折方法比数据分区（96.11％）具有更高的准确率97.03％。

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《International Conference on Communication Systems and Network Technologies》|2018年|151-155|共5页
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Syed Muzamil Basha; Dharmendra Singh Rajput;
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feature extraction; learning (artificial intelligence); pattern classification;

机译：特征提取;学习（人工智能）;模式分类;

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Evaluating the Importance of each Feature in Classification task

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