A Parallel Approach for Decision Trees Learning from Big Data Streams

Calistru, Ionel Tudor; Cotofrei, Paul; Stoffel, Kilian

doi:10.1007/978-3-319-19027-3_1

Cited by 2 publications

(2 citation statements)

References 16 publications

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“…Decision trees are one of the efficient techniques that are widely used in various areas, like machine learning, image processing, and pattern recognition. Decision trees are good due to having better comprehensibility of classification in terms of extracting from feature-based samples [ 1 , 2 , 3 ]. In addition, decision trees were not only proven efficient in many fields [ 4 ], but also have less parameters [ 5 ].…”

Section: Introductionmentioning

confidence: 99%

Evidential Decision Tree Based on Belief Entropy

Deng

2019

Entropy

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Decision Tree is widely applied in many areas, such as classification and recognition. Traditional information entropy and Pearson’s correlation coefficient are often applied as measures of splitting rules to find the best splitting attribute. However, these methods can not handle uncertainty, since the relation between attributes and the degree of disorder of attributes can not be measured by them. Motivated by the idea of Deng Entropy, it can measure the uncertain degree of Basic Belief Assignment (BBA) in terms of uncertain problems. In this paper, Deng entropy is used as a measure of splitting rules to construct an evidential decision tree for fuzzy dataset classification. Compared to traditional combination rules used for combination of BBAs, the evidential decision tree can be applied to classification directly, which efficiently reduces the complexity of the algorithm. In addition, the experiments are conducted on iris dataset to build an evidential decision tree that achieves the goal of more accurate classification.

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Section: Introductionmentioning

confidence: 99%

Evidential Decision Tree Based on Belief Entropy

Deng

2019

Entropy

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“…Using a scalable regression tree algorithm, Yin et al presented an open‐source implement of the PLANET on the basis of MapReduce framework of the Hadoop, but the impact of Hadoop Distributed File System (HDFS) input/output performance remained to be improved. The solution proposed by Calistru et al was a method to adapt the dsCART algorithm to horizontal parallelism by implementing the MapReduce programming model. It should be noted that some parameters in this solution would influence the behavior of the algorithm seriously.…”

Section: Introductionmentioning

confidence: 99%

A parallel C4.5 decision tree algorithm based on MapReduce

Liu

Yang

et al. 2017

Concurrency and Computation

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Summary In the supervised classification, large training data are very common, and decision trees are widely used. However, as some bottlenecks such as memory restrictions, time complexity, or data complexity, many supervised classifiers including classical C4.5 tree cannot directly handle big data. One solution for this problem is to design a highly parallelized learning algorithm. Motivated by this, we propose a parallelized C4.5 decision tree algorithm based on MapReduce (MR‐C4.5‐Tree) with 2 parallelized methods to build the tree nodes. First, an information entropy‐based parallelized attribute selection method (MR‐A‐S) on several subsets for MR‐C4.5‐Tree is proposed to confirm the best splitting attribute and the cut points. Then, a data splitting method (MR‐D‐S) in parallel is presented to partition the training data into subsets. At last, we introduce the MR‐C4.5‐Tree learning algorithm that grows in a top‐down recursive way. Besides, the depth of the constructed decision tree, the number of samples and the maximal class probability in each tree node are used as the termination conditions to avoid the over‐partitioning problem. Experimental studies show the feasibility and the good performance of the proposed parallelized MR‐C4.5‐Tree algorithm.

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A Parallel Approach for Decision Trees Learning from Big Data Streams

Cited by 2 publications

References 16 publications

Evidential Decision Tree Based on Belief Entropy

Evidential Decision Tree Based on Belief Entropy

A parallel C4.5 decision tree algorithm based on MapReduce

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