Scalable Fuzzy Decision Tree Induction Using Fast Data Partitioning and Incremental Approach for Large Dataset

الموضوعات :

Somayeh Lotfi ¹ , Mohammad Ghasemzadeh ² , Mehran Mohsenzadeh ³ , Mitra Mirzarezaee ⁴

1 - Department of Computer Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran
2 - Computer Engineering Department, Yazd University, Yazd, Iran.
3 - Department of Computer Engineering, Science and Research Branch, Islamic Azad University, Tehran ,IRAN
4 - Department of Computer Engineering, Science and Research Branch, Islamic Azad University, Daneshgah Blvd., Simon Bulivar Blvd., P.O. Box: 14515/775, Tehran, Iran

تاريخ الإرسال : 14 الثلاثاء , جمادى الأولى, 1442 تاريخ التأكيد : 29 الأحد , شعبان, 1442 تاريخ الإصدار : 19 الإثنين , جمادى الثانية, 1442

الکلمات المفتاحية: Large dataset, Fuzzy Entropy, Fuzzy Decision trees, Fuzzy partitioning,

ملخص المقالة :

The decision tree is one of the popular methods for learning and reasoning through recursive partitioning of data space. To choose the best attribute in the case on numerical features, partitioning criteria should be calculated for individual values or the value range of each attribute should be divided into two or more intervals using a set of cut points. In partitioning range of attribute, the fuzzy partitioning can be used to reduce the noise sensitivity of data and to increase the stability of decision trees. Since the tree-building algorithms need to keep in main memory the whole training dataset, they have memory restrictions. In this paper, we present an algorithm that builds the fuzzy decision tree on the large dataset. In order to avoid storing the entire training dataset in main memory and overcome the memory limitation, the algorithm builds DTs in an incremental way. In the discretization stage, a fuzzy partition was generated on each continuous attribute based on fuzzy entropy. Then, in order to select the best feature for branches, two criteria, including fuzzy information gain and occurrence matrix are used. Besides, real datasets are used to evaluate the behavior of the algorithm in terms of classification accuracy, decision tree complexity, and execution time as well. The results show that proposed algorithm without a need to store the entire dataset in memory and reduce the complexity of the tree is able to overcome the memory limitation and making balance between accuracy and complexity .

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