An Exploratory Analysis between the Feature Selection Algorithms IGMBD and IGChiMerge
作者: P.KalpanaK.Mani
刊名: International Journal of Information Technology and Computer Science(IJITCS), 2017, Vol.9 (7), pp.61-68
来源数据库: Modern Education & Computer Science(MECS)Journal
DOI: 10.5815/ijitcs.2017.07.07
原始语种摘要: Most of the data mining and machine learning algorithms will work better with discrete data rather than continuous. But the real time data need not be always discrete and thus it is necessary to discretize the continuous features. There are several discretization methods available in the literature. This paper compares the two methods Median Based Discretization and ChiMerge discretization. The discretized values obtained using both methods are used to find the feature relevance using Information Gain. Using the feature relevance, the original features are ranked by both methods and the top ranked attributes are selected as the more relevant ones. The selected attributes are then fed into the Naive Bayesian Classifier to determine the predictive accuracy. The experimental results clearly...
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  • Selection 分选
  • features 特征
  • selected 被选
  • discretize 离散化
  • Analysis 分析
  • machine 机器
  • discretization 离散化
  • continuous 连续的
  • accuracy 准确度
  • relevance 关联性