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An application of locally linear model tree algorithm with combination of feature selection in credit scoring

INTERNATIONAL JOURNAL OF SYSTEMS SCIENCE 2014年第10期45卷 2213-2222页

作者： Siami, Mohammad Gholamian, Mohammad Reza Basiri, Javad Iran Univ Sci & Technol Sch Ind Engn Tehran Iran Univ Tehran Dept Elect & Comp Engn Tehran Iran

Nowadays, credit scoring is one of the most important topics in the banking sector. Credit scoring models have been widely used to facilitate the process of credit assessing. In this paper, an application of the locally linear model tree algorithm (LOLIMOT) was experimented to evaluate the superiority of its performance to predict the customer's credit status. The algorithm is improved with an aim of adjustment by credit scoring domain by means of data fusion and feature selection techniques. Two real world credit data sets - Australian and German - from UCI machine learning database were selected to demonstrate the performance of our new classifier. The analytical results indicate that the improved LOLIMOT significantly increase the prediction accuracy.

关键词： credit scoring locally linear model tree classification finance and banking

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An Application of locally linear model tree Algorithm for Predictive Accuracy of Credit Scoring

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1st International Conference on model and Data Engineering (MEDI 2011)

作者： Siami, Mohammad Gholamian, Mohammad Reza Basiri, Javad Fathian, Mohammad Iran Univ Sci & Technol Dept Ind Engn Tehran Iran

ISBN: (纸本)9783642244421;9783642244438

Economical crisis in recent years leads the banks to pay more attention to credit risk assessment. Financial institutes have used various kinds of decision support systems, to reduce their credit risk. Credit scoring is one of the most important systems that have been used by the banks and financial institutes. In this paper, an application of locally linear model tree (LOLIMOT) algorithm was experimented to improve the predictive accuracy of credit scoring. Using the Australian credit data from UCI machine learning database repository, the algorithm was found an increase in predictive accuracy in comparison with some other well-known methods in the credit scoring area. The experiments also indicate that LOLIMOT get the best result in terms of average accuracy and type I and II error.

关键词： data mining credit scoring locally linear model tree classification finance and banking

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Prediction Of Financial Time Series With Recurrent LoLiMot (locally linear model tree)

Prediction Of Financial Time Series With Recurrent LoLiMot (...

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2nd International Conference on Computer and Automation Engineering (ICCAE)

作者： Chegini, Hossein Lucas, Caro Islamic Azad Univ Tehran Sci & Res Branch Comp Sci Tehran Iran Univ Tehran Elect & Comp Engn Dept Control & Intelligent Proc Ctr Excellence Tehran Iran

ISBN: (纸本)9781424455690

The tolerance and non- stability in financial indexes make changes to other sub- systems like human resources, economics, factory productions and etc. Having underling knowledge and a model to simulate such systems obtains a fine vision to estimate further and calculate hard-decision making tasks before execution like: dept from banks, cash injecting and insurance services. Using Neuro-fuzzy networks are one of the most powerful tools for this estimation. The particular locally linear model type of these networks called LoLiMot are in interest because of their linear training and construction optimization. These network can be much efficient when be a recurrent network why can better capture the dynamism's order of dynamic processes. The locally linear Neuro-Fuzzy model ( LoLiMot) here is as basis for making recurrent. In this paper this network with a global state feedback is implemented and the accuracy and the results of this recurrent network on Dow Jones index as a financial time series are compared with the static LoLiMot. The obtained results were better.

关键词： prediction neuro-fuzzy recurrent networks time series locally linear model tree forecasting

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SHORT-TERM AND LONG-TERM THERMAL PREDICTION OF A WALKING BEAM FURNACE USING NEURO-FUZZY TECHNIQUES

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THERMAL SCIENCE 2015年第2期19卷 703-721页

作者： Banadaki, Hamed Dehghan Nozari, Hasan Abbasi Shoorehdeli, Mandi Aliyari Islamic Azad Univ Ashkezar Branch Dept Elect Engn Yazd Iran Islamic Azad Univ Joybar Branch Dept Elect Engn Joybar Iran KN Toosi Univ Fac Elect Engn Dept Mechatron Tehran Iran

The walking beam furnace is one of the most prominent process plants often met in an alloy steel production factory and characterised by high non-linearity, strong coupling, time delay, large time-constant and time variation in its parameter set and structure. From another viewpoint, the walking beam furnace is a distributed-parameter process in which the distribution of temperature is not uniform. Hence, this process plant has complicated non-linear dynamic equations that have not worked out yet. In this paper, we propose one-step non-linear predictive model for a real walking beam furnace using non-linear black-box subsystem identification based on locally linear neuro-fuzzy model. Furthermore, a multi-step predictive model with a precise long prediction horizon (i. e., ninety seconds ahead), developed with application of the sequential one-step predictive models, is also presented for the first time. The locally linear model tree which is a progressive tree-based algorithm trains these models. Comparing the performance of the one-step linear neuro-fuzzy model predictive models with their associated models obtained through least squares error solution proves that all operating zones of the walking beam furnace are of non-linear sub-systems. The recorded data from Iran Alloy Steel factory is utilized for identification and evaluation of the proposed neuro-fuzzy predictive models of the walking beam furnace process.

关键词： non-linear prediction walking beam furnace locally linear neuro-fuzzy locally linear model tree least-squares error

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The Application of the locally linear model tree on Customer Churn Prediction

The Application of the Locally Linear Model Tree on Customer...

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International Conference of Soft Computing and Pattern Recognition

作者： Ghorbani, Amineh Taghiyareh, Fattaneh Lucas, Caro Delft Univ Technol Fac Technol Policy & Management Delft Netherlands Univ Tehran Dept Elect & Comp Sci Tehran Iran Univ Tehran Control & Intelligent Processing Ctr Excellence Tehran Iran

ISBN: (纸本)9781424453306

Acquiring new customers in any business is much more expensive than trying to keep the existing ones. Thus many prediction models are presented to detect churning customers. The objective of this paper was to improve the predictive accuracy and interpretability of churn detection. For this purpose, the application of the locally linear model tree (LOLIMOT) algorithm, which integrates the advantage of neural networks, tree model and fuzzy modeling, was experimented. Applied to the data of a major telecommunication company, the method is found to improve prediction accuracy significantly compared to other algorithms, such as artificial neural networks, decision trees, and logistic regression. The results also indicate that LOLIMOT can have accurate outcome in extremely unbalanced datasets.

关键词： customer churn prediction locally linear model tree

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Intelligent computationally efficient modelling of multi-input multi-output non- linear dynamical process plants: An industrial steam generator case study

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PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART I-JOURNAL OF SYSTEMS AND CONTROL ENGINEERING 2014年第5期228卷 278-294页

作者： Nozari, Hasan A. Banadaki, Hamed D. Islamic Azad Univ Sari Branch Young Researchers & Elites Club Sari Iran Islamic Azad Univ Young Researchers & Elites Club Yazd Branch Yazd Iran

Most of the process plants have intrinsic non-linear, time-varying, non-minimum phase and coupling characteristics that result in a highly time-consuming and tedious task to derive complicated non-linear dynamic equations governing on such types of processes. On the other hand, even though such equations can be analytically extracted, they have not worked out in most cases yet. To find a simple fix that can address these hurdles associated with analytical modelling, two types of non-analytical models, namely, simulator and predictor, are proposed based on a computationally efficient technique so-called locally linear neuro-fuzzy modelling. That is, a simulator model as well as a specially structured long-term predictor model that is built based on a sequential arrangement of single-stage predictor models is presented for multi-input multi-output non-linear dynamical process plants and feasibly applied to a real water-tube steam generator for the first time. An adaptive evolving algorithm named linear model tree contributes to estimate the parameters of neuro-fuzzy simulator and predictor models. Furthermore, an order selection method based on Lipschitz theory, whose merit is being totally independent from developing any model prior to commencing tedious modelling trials, is also proposed for the first time by defining a modified Lipschitz index to remedy the order determination problem within the very first steps of black-box non-linear system identification. The blend of such a fully modelling-independent order selection algorithm and a unique type of computationally inexpensive non-linear neuro-fuzzy model lessens the overall computational burden of modelling procedure that represents a great concern in a black-box system identification approach. The recorded data from a real water-tube steam generator operating at Abbot Power plant unit of Champaign, IL, were exploited to carry out experimental modelling in order to reveal the pros and cons of the presented model

关键词： Water-tube steam generator non-linear process identification locally linear neuro-fuzzy network locally linear model tree model-based predictive control fault diagnosis

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A Local linear Neurofuzzy model for the Prediction of Permeability from Well-log Data in Carbonate Reservoirs

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PETROLEUM SCIENCE AND TECHNOLOGY 2013年第5期31卷 448-457页

作者： Aghbelagh, Y. Beiraghdar Nabi-Bidhendi, M. Lucas, C. Univ Tehran Inst Geophys Tehran Iran Univ Tehran Control & Intelligent Proc Ctr Excellence Elect & Comp Engn Dept Tehran Iran

The prediction of permeability in heterogeneous carbonates from well-log data represents a difficult and complex problem. The authors present a new approach for the prediction of permeability. It is based on the recently developed neurofuzzy interpretation of locally linear models, which has led to the introduction of the intuitive incremental learning algorithm referred to as the locally linear model tree. The incremental learning algorithm initializes the model with an optimal linear least-squares estimate and automatically increases the number of neurons in each epoch. The model is optimized for the number of neurons to avoid overfitting and to provide maximum generalization by considering the error index of validation sets during training. The effectiveness of the methodology is demonstrated with a case study in a heterogeneous carbonate reservoir (Fahliyan formation in southwest Iran). Wireline data and core data sets from three wells that are located in the center of the field provide the data for the learning task. Core permeability and well-log data from a forth well provide the basis for model validation. The numerical results of the neurofuzzy modeling method are compared with the results of multiple linear regression and artificial neural network. The results are very satisfactory and open the possibilities for future research and applications.

关键词： artificial neural network locally linear model tree neurofuzzy permeability

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A Comparative Study on Forecasting Polyester Chips Prices for 15 Days, Using Different Hybrid Intelligent Systems

A Comparative Study on Forecasting Polyester Chips Prices fo...

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International Joint Conference on Neural Networks (IJCNN)

作者： Fazli, Mojtaba Sedigh Lebraty, Jean-Fabrice Univ Montesquieu Bordeaux 4 Bordeaux France Lyon3 Univ IAE Business Sch Lab Magellan EA3713 Lyon France

ISBN: (纸本)9781467361293;9781467361286

Forecasting in a risky situation is a very important function for managers to assist them in decisionmaking. One of the fluctuated markets in stock exchange market is chemical market. In this research the target item for prediction is PET (Poly Ethylene Terephthalate) which is the raw material for textile industries and it's very sensitive on oil prices and the demand and supply ratio. The main idea is coming through NORN model which was presented by Lee and Liu [1]. In this article after modifying the NORN model, a model has been proposed and real data are applied to this new model (we named it AHIS which stands for Adaptive Hybrid Intelligent System). Finally, three different types of simulation have been conducted and compared with each other. They show that hybrid model which is supporting both Fuzzy Systems and Neural Networks concepts, satisfied the research question considerably. In normal situation the model forecasts a relevant trend and can be used as a DSS for a manager.

关键词： Efficient Market Hypothesis Financial Forecasting Chemicals Artificial Intelligence Artificial Neural Networks Decision Support System locally linear model tree Hybrid Neuro Fuzzy model

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NEURO-FUZZY BASED FAULT DIAGNOSIS OF A STEAM GENERATOR

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IFAC Proceedings Volumes 2009年第8期42卷 1180-1185页

作者： Roozbeh Razavi-Far Hadi Davilu Vasile Palade Caro Lucas Department of Nuclear Engineering Amirkabir University of Technology P.O. Box 15875-4413 Tehran Iran (Tel:+98-***) Computing Laboratory Oxford University Wolfson Building Parks Road Oxford OX1 3QD UK Center of Excellence on Control and Intelligent Processing Department of Electrical and Computer Engineering University of Tehran P.O. Box 14395-1515 Tehran Iran

This paper focuses on the development and application of a Neuro-Fuzzy (NF) networks-based scheme for Fault Detection and Isolation (FDI) in a U-tube Steam Generator (UTSG). First, a NF network is trained with data collected from a full scale UTSG simulator, and residuals are generated for fault detection. To identify the UTSG, a locally linear Neuro-Fuzzy (LLNF) model is used. This model is trained using the locally linear model tree (LOLIMOT) algorithm which is an incremental tree structure algorithm. Then, an evolutionary algorithm is used to train a Mamdani type NF network to classify the residuals. The residuals are analyzed by using this NF classifier for fault isolation purposes.

关键词： Fault Diagnosis Neuro-Fuzzy Networks locally linear Neuro Fuzzy model locally linear model tree Steam Generator

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Predictive control of a fossil power plant based on locally linear model tree (LOLIMOT)

Predictive control of a fossil power plant based on locally ...

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IEEE Conference on Control Applications

作者： Jalili-Kharaajoo, M Ranji, R Bagherzadeh, H Univ Teheran Control & Intelligent Proc Ctr Excellence Tehran Iran

ISBN: (纸本)078037729X

A predictive control algorithm based on locally linear model tree model (LOLIMOT) is implemented to control a fossil fuel power unite. The controller is a non-model based system that uses a LOLIMOT identifier to predict the response of the plant in a future time interval. An evolutionary programming (EP) approach, optimizes the identifier-predicted outputs and determines input sequence in a time window. This intelligent system provides a predictive control of multi-input multi-output nonlinear systems with slow time variation.

关键词： predictive control power plant evolutionary programming locally linear model tree

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