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دسته بندی:
داده های بزرگ - big data
سال انتشار:
2018
عنوان انگلیسی مقاله:
On Distributed Fuzzy Decision Trees for Big Data
ترجمه فارسی عنوان مقاله:
درخت تصمیم گیری فازی توزیع شده برای داده های بزرگ
منبع:
IEEE - IEEE TRANSACTIONS ON FUZZY SYSTEMS, VOL: 26, NO: 1, FEBRUARY 2018
نویسنده:
Armando Segatori, Francesco Marcelloni، Witold Pedrycz
چکیده انگلیسی:
Fuzzy decision trees (FDTs) have shown to be an effective solution in the framework of fuzzy classification. The approaches proposed so far to FDT learning, however, have generally
neglected time and space requirements. In this paper, we propose a
distributed FDT learning scheme shaped according to the MapReduceprogrammingmodelforgeneratingbothbinaryandmultiway
FDTs from big data. The scheme relies on a novel distributed fuzzy
discretizer that generates a strong fuzzy partition for each continuous attribute based on fuzzy information entropy. The fuzzy
partitions are, therefore, used as an input to the FDT learning algorithm, which employs fuzzy information gain for selecting the
attributes at the decision nodes. We have implemented the FDT
learning scheme on the Apache Spark framework. We have used
ten real-world publicly available big datasets for evaluating the
behavior of the scheme along three dimensions: 1) performance in
terms of classification accuracy, model complexity, and execution
time; 2) scalability varying the number of computing units; and
3) ability to efficiently accommodate an increasing dataset size.
We have demonstrated that the proposed scheme turns out to be
suitable for managing big datasets even with a modest commodity
hardware support. Finally, we have used the distributed decision
tree learning algorithm implemented in the MLLib library and the
Chi-FRBCS-BigData algorithm, a MapReduce distributed fuzzy
rule-based classification system, for comparative analysis
Index Terms: Apache spark, big data, fuzzy decision trees (FDTs), fuzzy discretizer, fuzzy entropy, fuzzy partitioning,MapReduce
قیمت: رایگان
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