A comparative study of algorithms constructing decision trees

Elaidi, Halima; Benabbou, Zahra; Abbar, Hassan

doi:10.1145/3230905.3230916

Cited by 8 publications

(10 citation statements)

References 7 publications

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“…Evaluasi dilakukan dengan menerapkan teknik klasifikasi yang melibatkan korelasi atribut spasial, yakni spatial decision tree. Algoritme tersebut merupakan pengembangan dari algoritme ID3 konvensional oleh Quinlan [20], dimana nilai entropy tidak diperoleh dari jumlah data melainkan dari jumlah ukuran spasial yang dihasilkan melalui interaksi antar atribut spasial [17]. Efektifitas algoritme ini dapat dikatakan cukup baik, yang dibuktikan dari penelitian sebelumnya pada klasifikasi spasial titik panas dengan menghasilkan akurasi yang cukup tinggi, yakni 74.72% [21], 87.69% [22], 71.66% [23], 75.66% [24].…”

Section: Pendahuluanunclassified

Algoritme Spatial Decision Tree untuk Evaluasi Kesesuaian Lahan Padi Sawah Irigasi

Nurkholis

Muhaqiqin

Susanto

2020

RESTI

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Agriculture has a strategic role in a country whereas food self-sufficiency being the main goal to be achieved. Indonesia has set a strategic plan for increasing the productivity of several commodities, including rice, especially irrigated lowland rice. That matter can be done by agricultural land extensification, which requires a land suitability directional map. This study aims to produce irrigated lowland rice land suitability maps which can be obtained by evaluation using spatial decision tree algorithm. The model is made in two different types, where model Y is an optimized version of model X. The dataset consists of two categories, namely eleven explanatory layers which are land and weather characteristics, and a target layer that represents irrigated lowland rice land suitability in study area of Grobogan Regency, Central Java Province. As an addition to planting requirements, two spatial weather datasets were generated using ordinary cokriging interpolation, which was not used in previous research, while actually being important element for determining plant timing an agricultural commodity. Based on accuracy, model Y is the best model with 96.67%, compared to model X with 86%. Both models make relief variable as the root node, but in spatial decision tree result, model X involves all variables, while model Y does not involve an elevation variable. The addition of weather variables in models is appropriate, as evidenced by the involvement in rules.

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Section: Pendahuluanunclassified

Algoritme Spatial Decision Tree untuk Evaluasi Kesesuaian Lahan Padi Sawah Irigasi

Nurkholis

Muhaqiqin

Susanto

2020

RESTI

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“…A study by building a multi-dimensional and multi-level credit risk ASTESJ ISSN: 2415-6698 indicator system aims to find the most important credit risk characteristics that will cause serious default risks. With the existence of several algorithms that can be used in research related to the C4.5 algorithm it has been done [3], which is where they compare the performance of ID3 with C4.5. And the results are better using C4.5 [4].…”

Section: Related Workmentioning

confidence: 99%

Utilization of Data Mining to Predict Non-Performing Loan

Widiyono¹,

Isa²

2020

Adv. sci. technol. eng. syst. j.

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In the banking industry, the existence of problem loans is inevitable. NPL (Non-Performing Loan) will certainly have an impact on the reduction in the capital of a bank. One good step in reducing the risk of credit default or the emergence of non-performing loans is to take proper care of debtors who begin to experience payment constraints. The main obstacle experienced in bank management, especially in the credit sector, is being unable to identify or detect potential debtors early due to a large amount of data and manual processing. In this study, the debtor payment history is presented as data to predict the existence of problem loans. History payment can be used to predict bad loans. The technic of data mining in this experiment is a new method. The results of research conducted using Naïve Bayes, Decision Tree, K-NN, Rule Induction, Logistic Regression, Random Forest, Generalized Linear Model, and Gradient Boosted Trees as a comparison then choose the method that has the highest accuracy to be implemented in making additional modules on the core banking system. Random Forest is the model that has the highest accuracy of 96.55%.

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“…The DTs can then be used to classify the remaining sets of test cases. The C5.0 algorithm offers a number of significant advantages over C4.5, since the rules generated are more accurate and it takes less time to generate them [25].…”

Section: Decision Tree and C50 Algorithmmentioning

confidence: 99%

A global prediction model for sudden stops of capital flows using decision trees

et al. 2020

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Capital flows is an important aspect of the international monetary system because they provide great direct and indirect benefits, and at the same time, they carry risks of vulnerability for countries with an open economy. Numerous works have studied the behavior of these flows and have developed models to predict sudden stop events. However, the existing models have limitations and the literature demands more research on the subject given that the accuracy of the models is still poor, and they have only been developed for emerging countries. This paper presents a new prediction model of sudden stop events of capital flows for both emerging countries and developed countries with the ability to estimate accurately future sudden stop scenarios globally. A sample of 103 countries was used, including 73 emerging countries and 30 developed countries, which has allowed the use of sample combinations that consider the regional heterogeneity of the warning indicators. To the sample under study, a method of decision trees has been applied, which has provided excellent prediction results given its ability to learn characteristics and create long-term dependencies from sequential data and time series. Our model has a great potential impact on the adequacy of macroeconomic policy against the risks derived from sudden stops of capital flows, providing tools that help to achieve financial stability at the global level.

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A comparative study of algorithms constructing decision trees

Cited by 8 publications

References 7 publications

Algoritme Spatial Decision Tree untuk Evaluasi Kesesuaian Lahan Padi Sawah Irigasi

Algoritme Spatial Decision Tree untuk Evaluasi Kesesuaian Lahan Padi Sawah Irigasi

Utilization of Data Mining to Predict Non-Performing Loan

A global prediction model for sudden stops of capital flows using decision trees

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