A new node splitting measure for decision tree construction

Chandra, B.; Kothari, Ravi; Paul, Pallath

doi:10.1016/j.patcog.2010.02.025

Cited by 71 publications

(39 citation statements)

References 11 publications

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“…Each decision tree in the random forest is constructed as a binary tree, its training set is produced by the bootstrapping technique and one sample can be repeated at most two times in each training set. To value the variable's splitting effect, we adopt distinct class based splitting measure [31], which consists of two parts: one is related to the number of sample classes in each child partition and the other is related to each class's proportion in the partition. To find meaningful metabolites which can discriminate liver diseases from the control, discriminate among HCC, CHB and CIR, further discriminate between each two different liver diseases, RF-RFE was conducted on five cases, labeled with RF-RFEi (i01, 2, 3, 4, and 5) in sequence.…”

Section: Discussionmentioning

confidence: 99%

Serum metabolomics reveals the deregulation of fatty acids metabolism in hepatocellular carcinoma and chronic liver diseases

et al. 2012

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Patients with chronic liver diseases (CLD) including chronic hepatitis B and hepatic cirrhosis (CIR) are the major high-risk population of hepatocellular carcinoma (HCC). The differential diagnosis between CLD and HCC is a challenge. This work aims to study the related metabolic deregulations in HCC and CLD to promote the discovery of the differential metabolites for distinguishing the different liver diseases. Serum metabolic profiling analysis from patients with CLD and HCC was performed using a liquid chromatography-mass spectrometry system. The acquired large amount of metabolic information was processed with the random forest-recursive feature elimination method to discover important metabolic changes. It was found that long-chain acylcarnitines accumulated, whereas free carnitine, medium and short-chain acylcarnitines decreased with the severity of the non-malignant liver diseases, accompanied with corresponding alterations of enzyme activities. However, the general changing extent was smaller in HCC than in CIR, possibly due to the special energy-consumption mechanism of tumor cells. These observations may help to understand the mechanism of HCC occurrence and progression on the metabolic level and provide information for the identification of early and differential metabolic markers for HCC.

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Section: Discussionmentioning

confidence: 99%

Serum metabolomics reveals the deregulation of fatty acids metabolism in hepatocellular carcinoma and chronic liver diseases

et al. 2012

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“…The first gene, criterion, is an integer that indexes one of the 15 splitting criteria we have implemented: information gain [29], Gini index [5], global mutual information [19], G statistics [26], Mantaras criterion [13], hypergeometric distribution [25], Chandra-Varghese criterion [10], DCSM [9], χ 2 [27], mean posterior improvement [33], normalized gain [21], orthogonal criterion [17], twoing [5], CAIR [11] and gain ratio [32].…”

Section: Split Genesmentioning

confidence: 99%

“…Examples are the Gini index [5], the twoing criterion [5], the orthogonality criterion [17], among others. We have also included lesser-known criteria such as CAIR [11] and mean posterior improvement [33], as well as the more recent Chandra-Varghese [10] and DCSCM [9], to enhance the diversity of options for generating splits in a decision tree.…”

Section: Split Genesmentioning

confidence: 99%

A hyper-heuristic evolutionary algorithm for automatically designing decision-tree algorithms

Barros

Basgalupp

Carvalho

et al. 2012

Proceedings of the 14th Annual Conference on Genetic and Evolutionary Computation

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Decision tree induction is one of the most employed methods to extract knowledge from data, since the representation of knowledge is very intuitive and easily understandable by humans. The most successful strategy for inducing decision trees, the greedy top-down approach, has been continuously improved by researchers over the years.This work, following recent breakthroughs in the automatic design of machine learning algorithms, proposes a hyper-heuristic evolutionary algorithm for automatically generating decision-tree induction algorithms, named HEAD-DT. We perform extensive experiments in 20 public data sets to assess the performance of HEAD-DT, and we compare it to the traditional decision-tree algorithms C4.5 and CART. Results show that HEAD-DT can generate algorithms that significantly outperform C4.5 and CART regarding predictive accuracy and F-Measure.

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“…The best attributes can be calculated through Gauss's theorem according to the available data samples. Mahmood, Imran and Satuluri [5] think that the introduction of Gini index (RGI), a new heuristic function for dimensionality reduction, could reduce the dimension of data and improve the accuracy of data classification. Chen and Xia [6] use the decision tree algorithm under the categorical variables, and introduce an optimized splitting function that constructs binary trees using domain variables, through which it can extract and select the prediction criterion.…”

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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A new node splitting measure for decision tree construction

Cited by 71 publications

References 11 publications

Serum metabolomics reveals the deregulation of fatty acids metabolism in hepatocellular carcinoma and chronic liver diseases

Serum metabolomics reveals the deregulation of fatty acids metabolism in hepatocellular carcinoma and chronic liver diseases

A hyper-heuristic evolutionary algorithm for automatically designing decision-tree algorithms

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

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