Mining the customer credit using classification and regression tree and multivariate adaptive regression splines

Lee, Tian–Shyug; Chiu, Chih‐Chou; Chou, Yu-Chao; Lu, Chi-Jie

doi:10.1016/j.csda.2004.11.006

Cited by 329 publications

(115 citation statements)

References 56 publications

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“…Because this system is obtained based on previous applicants informat ion, performs similarly for different applicant in the same situations; unlike other personal decision-making methods which are changed by personal opinions. In addit ion, since banks tries preventing the risk of no fundable credits, intelligent systems could recognize the credit with high p robability of default [1,3].…”

Section: Introductionmentioning

confidence: 99%

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Bank Customer Credit Scoring by Using Fuzzy Expert System

Bazmara¹,

Donighi²

2014

IJISA

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Abstract-Granting banking facility is one of the most important parts of the financial supplies for each bank. So this activity becomes more valuable economically and always has a degree of risk. These days several various developed Artificial Intelligent systems like Neural Network, Decision Tree, Logistic Regression Analysis, Linear Discriminant Analysis and etc, are used in the field of granting facilities that each of this system owns its advantages and disadvantages. But still studying and working are needed to improve the accuracy and performance of them. In this article among other AI methods, fuzzy expert system is selected. This system is based on data and also extracts rules by using data. Therefore the dependency to experts is omitted and interpretability of rules is obtained. Validity of these rules could be confirmed or rejected by banking affair experts.For investigating the performance of proposed system, this system and some other methods were performed on various datasets. Results show that the proposed algorithm obtained better performance among the others.

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Section: Introductionmentioning

confidence: 99%

“…In contemporary banking systems, traditional decision making methods have been excluded and intelligent systems are often used [1]. Increasing accuracy, omitt ing personal judgments and decreasing decision making time are the advantages of such systems.…”

Section: Introductionmentioning

confidence: 99%

Bank Customer Credit Scoring by Using Fuzzy Expert System

Bazmara¹,

Donighi²

2014

IJISA

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“…First, the model containing too many basis functions that lead to its overfitting is created. At this stage, it is also possible to take into account interactions between predictors or they can constitute only additive components [7]. At the second stage of the algorithm execution (pruning), these basis functions that contribute least to the goodness-of-fit are removed [8].…”

Section: Multivariate Adaptive Regression Splinesmentioning

confidence: 99%

Examples of the Use of Data Mining Methods in Animal Breeding

Grzesiak¹,

Zaborski²

2012

Data Mining Applications in Engineering and Medicine

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“…19−21 Compared to other statistical tools such as linear regression and discriminant analysis, decision tree offers several major advantages, including the flexibility with handling of different types of response variables, the capability of identifying complicated relationship without strong model assumptions, the robustness with respect to outliers, the ability to deal with missing values, and easy result interpretation. 17,22 Classic decision trees such as the classification and regression tree (CART) 23 and "C5" 24 are constructed by selecting the best split measured by the Gini index or information gain through exhaustive search over all possible variables to split and all possible places for a split. These algorithms suffer from two major limitations: overfitting, 25 and biased variable selection, that is, the preference for variables with more categories and the continuous variables.…”

Section: ■ Introductionmentioning

confidence: 99%

Application of Stochastic Models in Identification and Apportionment of Heavy Metal Pollution Sources in the Surface Soils of a Large-Scale Region

Cheng

2013

Environ. Sci. Technol.

225

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As heavy metals occur naturally in soils at measurable concentrations and their natural background contents have significant spatial variations, identification and apportionment of heavy metal pollution sources across largescale regions is a challenging task. Stochastic models, including the recently developed conditional inference tree (CIT) and the finite mixture distribution model (FMDM), were applied to identify the sources of heavy metals found in the surface soils of the Pearl River Delta, China, and to apportion the contributions from natural background and human activities. Regression trees were successfully developed for the concentrations of Cd, Cu, Zn, Pb, Cr, Ni, As, and Hg in 227 soil samples from a region of over 7.2 × 10 4 km 2 based on seven specific predictors relevant to the source and behavior of heavy metals: land use, soil type, soil organic carbon content, population density, gross domestic product per capita, and the lengths and classes of the roads surrounding the sampling sites. The CIT and FMDM results consistently indicate that Cd, Zn, Cu, Pb, and Cr in the surface soils of the PRD were contributed largely by anthropogenic sources, whereas As, Ni, and Hg in the surface soils mostly originated from the soil parent materials. ■ INTRODUCTIONHeavy metals and their compounds are naturally ubiquitous throughout the environment. Heavy metals in soils come in the first place from weathering of the parent materials but can also result from accumulation of the metals released from industrial and agricultural activities. According to Alloway, 1 the major anthropogenic inputs of heavy metals to soils and the environment are metalliferous mining and smelting, agricultural and horticultural materials, sewage sludges, fossil fuel combustion, metallurgical industries, electronics, chemical and other manufacturing industries, waste disposal, sports shooting and fishing, and warfare and military training. Globally, mine tailings, smelter emissions, waste incineration, and atmospheric deposition are the most important sources of heavy metal pollution, whereas vehicular emissions are widely accepted as the main sources of heavy metals in urban topsoils and dust.2 In China, the major sources of heavy metal pollution are emissions from industrial operations, whereas wastewater irrigation and land fertilization with sludges contribute most to the heavy metal pollution of agricultural soils.

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Mining the customer credit using classification and regression tree and multivariate adaptive regression splines

Cited by 329 publications

References 56 publications

Bank Customer Credit Scoring by Using Fuzzy Expert System

Bank Customer Credit Scoring by Using Fuzzy Expert System

Examples of the Use of Data Mining Methods in Animal Breeding

Application of Stochastic Models in Identification and Apportionment of Heavy Metal Pollution Sources in the Surface Soils of a Large-Scale Region

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