Towards the automatic design of decision tree induction algorithms

Barros, Rodrigo C.; Basgalupp, Márcio P.; Carvalho, André C. P. L. F. de; Freitas, Alex A.

doi:10.1145/2001858.2002050

Cited by 21 publications

(13 citation statements)

References 25 publications

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“…The remaining algorithms use as training set the sum of both sub-training and validation sets. For each algorithm's execution, we have measured classification accuracy, F-Measure, 3 recall, precision, tree size and tree depth. The machine used to perform the experiments is a cluster that consists of 31 or so hyper-threaded (HT) Pentium-4 machines running at 3.2 GHz.…”

Section: Experimental Set Upmentioning

confidence: 99%

“…In the sec- 2 We set up GALE with its orthogonal version (one attribute per node split), because LEGAL-Tree and the remaining baseline algorithms implement orthogonal decision trees. 3 Since F-Measure, recall and precision are measures used in binary class problems, for multi-class problems we have considered each class to be the positive class, and the remaining the negative class (problems with n classes will have n distinct values for these measures). Values reported are the average of these n values.…”

Section: Experimental Set Upmentioning

confidence: 99%

“…As a result of this global search, EAs tend to cope better with attribute interactions than greedy methods [30]. Indeed, EAs have been successfully applied to data mining [65,47,17], in general, and to the induction of decision trees [42,45,71,26,14,49,50,72,11,3], in specific.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Evolving decision trees with beam search-based initialization and lexicographic multi-objective evaluation

Basgalupp

Barros

Carvalho

et al. 2014

Information Sciences

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Section: Experimental Set Upmentioning

confidence: 99%

Section: Experimental Set Upmentioning

confidence: 99%

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Evolving decision trees with beam search-based initialization and lexicographic multi-objective evaluation

Basgalupp

Barros

Carvalho

et al. 2014

Information Sciences

Self Cite

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“…In recent years, with the rapid development of computer networks and data storage technology, the amount of information data has been growing in a geometric progression [2]. To process the massive data, a single computer running time will be very long using the current data mining serial algorithm, which makes it difficult to meet the needs of data processing.…”

Section: Introductionmentioning

confidence: 99%

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

Zhu¹

2018

IJPE

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A CART decision tree algorithm based on attribute weight is proposed in this paper because of the present problems of complex classification, poor accuracy, low efficiency, and severe memory consumption of CART decision. What is more, the algorithm is combined with the parallel computing model of MapReduce. Theory of attribute weights is used in the algorithm. A decision tree is built through the sum of weights, which is decided by the degree that the attributes affect a decision. Thus the accuracy of classification through decision tree is improved. Parallel sorting algorithms of CART decision tree for massive data is implemented through the MapReduce programming technology of cloud computing. All the results of theoretical analysis and experimental comparison show that it is very important to mark attributes by weights through MapReduce. Furthermore, the accuracy of the classification of large sample data sets is improved significantly, classification efficiency of decision tree is improved and the trained time is also significantly reduced.

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“…Classification rule mining aims to discover a small set of rules in the database that forms an accurate classifier [4]. Recently, techniques and algorithms for classification rule mining have been intensively studied due to the large variety of practical applications for them [1], [3], [5], [6], [7]. Genetic Algorithms (GAs) are methods based on biological mechanisms, such as, Mendel"s laws and Darwin"s fundamental principle of natural selection.…”

Section: Introductionmentioning

confidence: 99%

A Genetic Algorithm for Discovering Classification Rules in Data Mining

Al-Maqaleh¹,

Shahbazkia²

2012

IJCA

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Data mining has as goal to discover knowledge from huge volume of data. Rule mining is considered as one of the usable mining method in order to obtain valuable knowledge from stored data on database systems. In this paper, a genetic algorithm-based approach for mining classification rules from large database is presented. For emphasizing on accuracy, coverage and comprehensibility of the rules and simplifying the implementation of a genetic algorithm. The design of encoding, genetic operators and fitness function of genetic algorithm for this task are discussed. Experimental results show that genetic algorithm proposed in this paper is suitable for classification rule mining and those rules discovered by the algorithm have higher classification performance to unknown data.

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Towards the automatic design of decision tree induction algorithms

Cited by 21 publications

References 25 publications

Evolving decision trees with beam search-based initialization and lexicographic multi-objective evaluation

Evolving decision trees with beam search-based initialization and lexicographic multi-objective evaluation

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

A Genetic Algorithm for Discovering Classification Rules in Data Mining

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