A Classification Algorithm of CART Decision Tree based on MapReduce Attribute Weights

Zhu, Fubao

doi:10.23940/ijpe.18.01.p3.1725

Cited by 10 publications

(7 citation statements)

References 18 publications

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“…The MapReduce programming paradigm is used for parallel and distributed processing of large datasets on a collection of computers ( [5], [25]). The framework is both exible and fault tolerant for data processing thereby allowing users to manage large amount of information [22].…”

Section: Mapreducementioning

confidence: 99%

Development of an Enhanced C4.5 Decision Tree Algorithm Using a Memoized Mapreduce Model

Paul

2023

Preprint

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Classification technique in data mining focuses on prediction which is done by classical C4.5 decision tree algorithm, but limited by its computation complexities due to large datasets. However, this results to inefficient implementation of the algorithm with reference to computing time, memory utilization and data complexity. Meanwhile, several researches have been done to curb these limitations. One of such improvements is the parallelizing of the algorithm using the MapReduce model. This involves splitting the large dataset into smaller units and distributing them on multiple computers for parallel processing, but the recursive nature of the algorithm makes the computational cost high due to large number of calculations that are repeated. This research is aimed at further reducing computation time, using memoized MapReduce model that involves storing the result of previous calculations in a cache. Thus, when same calculations re-occur, the cached result is returned, thereby eliminating re-computation.

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Section: Mapreducementioning

confidence: 99%

Development of an Enhanced C4.5 Decision Tree Algorithm Using a Memoized Mapreduce Model

Paul

2023

Preprint

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“…The MapReduce programming paradigm is used in Hadoop for the parallel and distributed processing of large datasets on a network of computers (Dean and Gemawat, 2008;Zhu et al, 2018). Users are able to manage a large amount of information because of its flexibility and capacity to tolerate faults while processing these data (Wang et al, 2014).…”

Section: Mapreducementioning

confidence: 99%

Development of an Enhanced C4.5 Decision Tree Algorithm Using a Memoized Mapreduce Model

Paul,

Obiniyi,

Donfack-Kana

et al. 2023

FJS

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Classification technique in data mining concentrates on the prediction of categorical or discrete target variables which is designed to be handled by the classical C4.5 decision tree algorithm, an algorithm whose aim is to produce a tree which accurately predicts the target variable for a new unseen data. However its recursive nature poses a limitation when huge volume of dataset is involved; making computation more complex and resulting in an inefficient implementation of the algorithm in terms of computing time, memory utilization and data complexity. Meanwhile, several researches have been done to control these limitations. One of such improvements is the parallelizing of the algorithm using the MapReduce model. This involves dividing the large dataset into smaller units and sharing them on multiple computers for parallel processing, but the recursive nature of the algorithm makes the cost of computing large number of repeated calculations quite high, which is our concern in this work. . This research is aimed at reducing computation time further, by using a memoized MapReduce model, which involves the saving of the result of previous calculations in a cache; hence, when same calculations are encountered again, the cached result is returned, thus re-computation is avoided. The cached result is considered a reduced cost compared to the computational cost of re-computation.

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“…(2) Where X : is the target attribute A : predictor attribute K : the number of members of X There are various types of decision tree algorithms for building tree structures, including the Iterative Dichotomizer 3 (ID3) algorithm (Yang et al, 2018), algorithm C4.5 (Sadiq & Ahmed, 2019), Classification and Regression tree (CART) algorithm (F. Zhu et al, 2018), algorithm (CHAID) (Saeed et al, 2020). One of the commonly used decision tree algorithms is the ID3 algorithm.…”

Section: Decisiontreementioning

confidence: 99%

Classification of tourism business certification institution auditors based on the decision tree algorithm C4.5: case study of LSUP XYZ

Mariani¹,

Sudjana²

2023

Mantik

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Auditors who have appropriate competence are needed to carry out certification. The amount of experience a person has before becoming an Auditor determines the competence possessed by an Auditor. To be assigned to carry out certification, an auditor needs special competencies such as areas of expertise, work experience, and the Schemes and Standards that become the reference for certification. Auditors owned by LSUP need to be classified so that they can be assigned according to their competence. The C4.5 Decision tree algorithm is one of the methods that can be used to create a model used to classify and predict auditor competence. 65 data from 221 data are used as training data to form a model in the form of a tree structure. This model consists of 3 attributes that form a Node in a tree structure, namely the Workplace, Skill and Standard attributes. Each attribute has a value that forms a branch. The model was evaluated using the Confusion Matrix with a matrix size of 7x7 which resulted in a TP value of 99, FP value of 121, FN value of 121, and TN value of 1199. The accuracy level of this model is quite good, namely 84%. But the Precise and Recall values are not very good, only 45%.

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A Classification Algorithm of CART Decision Tree based on MapReduce Attribute Weights

Cited by 10 publications

References 18 publications

Development of an Enhanced C4.5 Decision Tree Algorithm Using a Memoized Mapreduce Model

Development of an Enhanced C4.5 Decision Tree Algorithm Using a Memoized Mapreduce Model

Development of an Enhanced C4.5 Decision Tree Algorithm Using a Memoized Mapreduce Model

Classification of tourism business certification institution auditors based on the decision tree algorithm C4.5: case study of LSUP XYZ

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