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1 |
Application of fuzzy decision tree in EOR screening assessment
کاربرد درخت تصمیم فازی در ارزیابی غربالگری EOR-2019 Ranking of best possible Enhanced Oil Recovery (EOR) technics for implementing on a target field is one of the
most important questions that should be answered by reservoir engineers. EOR screening can be considered as a
tool for recommending the most appropriate EOR methods. Although for each candidate reservoir, the applicability
of EOR processes must be investigated specifically, EOR screening can be used as an indicator before
economic evaluations or reservoir descriptions are done and executive decisions are made. Implementing an
EOR project for predictions that pass this screening is the next step.
In this study, the fuzzy decision tree method (with the ability to rank and classify EOR methods simultaneously)
is introduced for EOR screening. Basic features for this study are permeability, viscosity, depth, temperature,
saturation, and API. Using a fuzzy decision tree enables us to design an expert system which generates
EOR rules automatically. This is one of the noticeable features of this study which reduces the importance of a
human expert role while designing the system and making it as expert as possible. Here, the fuzzy decision tree
method is implemented on a dataset consisting of 548 observations related to 10 different EOR techniques.
Predictions made by this method which are ranked from the most applicable EOR method to the least one
include the EOR method mentioned in the dataset for every observation in both training and test set. Moreover,
using the procedure introduced here for training the trees enables the expert system to be adaptive whenever the
dataset is updated Keywords: Data mining | Expert system | Artificial intelligence algorithms | EOR screening | Fuzzy logic | Fuzzy decision tree | Automatic rule generation |
مقاله انگلیسی |
2 |
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 |
3 |
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 |