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ردیف | عنوان | نوع |
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1 |
On Distributed Fuzzy Decision Trees for Big Data
درخت تصمیم گیری فازی توزیع شده برای داده های بزرگ-2018 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 |
مقاله انگلیسی |
2 |
On Distributed Fuzzy Decision Trees for Big Data
درخت تصمیم گیری فازی توزیع شده برای داده های بزرگ-2017 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 MapReduce programming model for generating both
binary and multi-way 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 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: i) performance in terms of
classification accuracy, model complexity and execution time,
ii) scalability varying the number of computing units and iii)
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 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.
Keywords: Fuzzy Decision Trees | Big Data | Fuzzy Entropy | Fuzzy Discretizer | Apache Spark | MapReduce | Fuzzy Partitioning |
مقاله انگلیسی |