Multiple Criteria Linear Programming Research Articles

Parallel RMCLP Classification Algorithm and Its Application on the Medical Data

To make better use of the cloud computing technology, and to overcome the computing and storage requirements which increase rapidly with the number of training samples, in this paper, a new parallel algorithm is proposed—Parallel Regularized Multiple-Criteria Linear Programming (PRMCLP) algorithm—The RMCLP model is converted into a unconstrained optimization problem, and then, in the parallel version, it is split into several tasks, where each part is mapped and computed on a separate processor. This approach enables us to obtain efficiently the final optimization solution of the whole classification problem. At last, we apply this algorithm to Medical data classification. All experiments show that our method and approach greatly increases the training speed of RMCLP in the parallel case.

A multi-class classification MCLP model with particle swarm optimization for network intrusion detection

The critical data we share through computer network gets stolen by unethical means. This unethical way of accessing one’s data without proper authentication becomes intrusion. To solve this issue, in this paper we propose a new network intrusion detection method, Multi-Class Classification Multiple Criteria Linear Programming (MCC-MCLP) model. MCLP is a mathematical classification technique that is used widely to solve real-time data mining problems. So far, the literature discusses only about binary classification MCLP. But in this paper we propose a Multi-Class Classification MCLP model. We use PSO for fine-tuning the parameters of MCC-MCLP. KDD CUP 99 data set is used for performance evaluation of the proposed method. Our MCC-MCLP method classifies the data better and helps in fine-tuning the parameters with the help of PSO. The results clearly show that the proposed model performs better in terms of detection rate, false alarm rate and accuracy.

An effective intrusion detection framework based on MCLP/SVM optimized by time-varying chaos particle swarm optimization

Many organizations recognize the necessities of utilizing sophisticated tools and systems to protect their computer networks and reduce the risk of compromising their information. Although many machine-learning-based data classification algorithm has been proposed in network intrusion detection problem, each of them has its own strengths and weaknesses. In this paper, we propose an effective intrusion detection framework by using a new adaptive, robust, precise optimization method, namely, time-varying chaos particle swarm optimization (TVCPSO) to simultaneously do parameter setting and feature selection for multiple criteria linear programming (MCLP) and support vector machine (SVM). In the proposed methods, a weighted objective function is provided, which takes into account trade-off between the maximizing the detection rate and minimizing the false alarm rate, along with considering the number of features. Furthermore, to make the particle swarm optimization algorithm faster in searching the optimum and avoid the search being trapped in local optimum, chaotic concept is adopted in PSO and time varying inertia weight and time varying acceleration coefficient is introduced. The performance of proposed methods has been evaluated by conducting experiments with the NSL-KDD dataset, which is derived and modified from well-known KDD cup 99 data sets. The empirical results show that the proposed method performs better in terms of having a high detection rate and a low false alarm rate when compared with the obtained results using all features.

Ambiguity Multi-attribute Decisions with Evidential Chains-based Fusion Reasoning Method

To learn the distribution of outcomes from previously ambiguity information for multi-attribute decision making, a novel associative Evidential Chains (ECs)-based fusion reasoning method was proposed. The convex combination of probabilistic beliefs from multiple refined ECs were induced by the proposed model based on multiple criteria linear programming. Its solution for the query case has varied flexibility with the similarity matrix derived from the historical ECs. Results of the applications with the benchmark cardiac diagnostic data sets verify that the proposed method is effective and interpretable.

A New Intrusion Detection Approach using PSO based Multiple Criteria Linear Programming

Abstract Intrusion detection system (IDS) is an inseparable part of each computer networks to monitor the events and attacks, which many researchers proposed variety of models to improve the performance of IDS. In this paper we present a new method based on multiple criteria linear programming and particle swarm optimization to enhance the accuracy of attacks detection. Multiple criteria linear programming is a classification method based on mathematical programming which has been showed a potential ability to solve real-life data mining problems. However, tuning its parameters is an essential steps in training phase. Particle swarm optimization (PSO) is a robust and simple to implement optimization technique has been used in order to improve the performance of MCLP classifier. KDD CUP 99 dataset used to evaluate the performance of proposed method. The result demonstrated the proposed model has comparable performance based on detection rate, false alarm rate and running time compare to two other benchmark classifiers.

Structural Regular Multiple Criteria Linear Programming for Classification Problem

Classification problem has attracted an increasing amount of interest. Various classifiers have been proposed in the last decade, such as ANNs, LDA, and SVM. Regular Multiple Criteria Linear Programming (RMCLP) is an effective classification method, which was proposed by Shi and his colleagues and have been applied to handle different real-life data mining problems. In this paper, inspired by the application potential of RMCLP, we propose a novel Structural RMCLP (called SRMCLP) method for classification problem. Unlike RMCLP, SRMCLP is sensitive to the structure of the data distribution and can construct more reasonable classifiers by exploiting these prior data distribution information within classes. The corresponding optimization problem of SRMCLP can be solved by a standard quadratic programming. The effectiveness of the proposed method is demonstrated via experiments on synthetic and available benchmark datasets.

Error Correction Method in Classification by Using Multiple-Criteria and Multiple-Constraint Levels Linear Programming

In classification based on multiple-criteria linear programming (MCLP), we need to find the optimal solution of the MCLP problem as a classifier. According to dual theory, multiple criteria can be switched to multiple constraint levels, and vice versa. A MCLP problem can be logically extended into a multiple-criteria and multiple-constraint levels linear programming (MC2LP) problem. In many applications, such as credit card account classification, how to handle two types of error is a key issue. The errors can be caused by a fixed cutoff between a "Good" group and a "Bad" group. Two types of error can be systematically corrected by using the structure of MC2LP, which allows two alterable cutoffs. In order to do so, a penalty (or cost) is imposed to find the potential solution for all possible trade-offs in solving MC2LP problem. Some correction strategies can be investigated by the solution procedure. Furthermore, a framework of decision supporting system can be illustrated for various real-life applications of the proposed method.

Parallel Regularized Multiple-criteria Linear Programming

In this paper, we proposed a new parallel algorithm: Parallel Regularized Multiple-Criteria Linear Programming (PRMCLP) to overcome the computing and storage requirements increased rapidly with the number of training samples. Firstly, we convert RMCLP model into a unconstrained optimization problem, and then split it into several parts, and each part is computed by a single processor. After that, we analyze each part's result for next cycle going. By doing this, we are be able to obtain the final optimization solution of the whole classification problem. All experiments in public datasets show that our method greatly increases the training speed of RMCLP in the help of multiple processors.

An Interactive Approach for Multicriteria Decision Making Using a Tchebycheff Utility Function Approximation

Multicriteria decision making (MCDM) can provide an efficient mean for considering various and conflicting objectives to reveal the alternative that maximizes the decision maker's (DM) utility. In this paper, we propose a new interactive MCDM method for implicit alternatives to help a DM obtain a most preferred solution. We employ a Tchebycheff function to generate weights for objectives consistent with the DM's responses to pairwise comparisons between alternatives and present a mixed integer linear programming formulation to generate these weights. Thus, we approximate the DM's utility function by a Tchebycheff function and generate weights consistent with the DM's responses. We test our approach with different true utility functions on various sized multiple criteria linear programming problems. The computational results show that even with non-Tchebycheff true utility functions, our method can generate alternatives very close to the optimal solution with few questions. The comparison of our results with other methods reveals its advantages. Copyright © 2013 John Wiley & Sons, Ltd.

Financial Distress Prediction for Chinese Listed Manufacturing Companies

Financial distress prediction can be formulated as a classification problem and accomplished by advanced data mining techniques. In classification based on multiple criteria linear programming (MCLP), we need to find the optimal solution as a classifier, by solving the MCLP problem. However, the errors can be caused by a fixed cutoff between a “good” group and a “bad” group by MCLP structure. In many applications, such as credit card account classification and bankruptcy prediction, how to handle two types of error is a key issue. Using the structure of multiple criteria and multiple constraint levels linear programming (MC2LP), which allows alterable cutoff, two types of errors can be systematically corrected. In order to do so, a penalty is imposed to find the potential solution for all possible trade-offs in solving MC2LP problem. Real dataset of Chinese listed manufacturing companies is used to validate MC2LP method. Comparison with classical optimization-based method SVM and MCLP is also provided.

The Application of Multiple Criteria Linear Programming in Advertisement Clicking Events Prediction

In advertisement industry, it is important to predict potentially profitable users who will click target ads (i.e., Behavioral Targeting). The task selects the potential users that are likely to click the ads by analyzing user's clicking/web browsing information and displaying the most relevant ads to them. In this paper, we present a Multiple Criteria Linear Programming (MCLP) prediction model as the solution. The experiment datasets are provided by a leading Internet company in China, and can be downloaded from track2 of the KDD Cup 2012 datasets. In this paper, Support Vector Machines (SVM), Logistic Regression (LR), Radial Basis Function Network (RBF Network), k-Nearest Neighbour algorithm (KNN) and NaïveBayes are used as five benchmark models for comparison. The results indicate that MCLP is a promising model in behavioral targeting tasks.

An Embedded Backward Feature Selection Method for MCLP Classification Algorithm

Feature selection is very crucial for improving classification performance, especially in the case of high-dimensional data classification.Different classification algorithms tend to select different optimal feature subsets. Based on detailed analysis of the characteristics of Multiple Criteria Linear Programming (MCLP) classification algorithm, a feature selection criterion is presented and an embedded backward feature selection procedure is designed for MCLP in this paper. Experiments on four datasets (artificial and real-world) are carried out, and the effectiveness of the presented method is assessed. Results show that it achieves good performance as expected.

A Data Mining Perspective of the Newsvendor Problem

This study examined a modified newsvendor problem. The modified newsvendor problem concerned with how wholesalers can achieve maximum profits. Instead of building a profit function for the wholesalers and finding the optimal solutions as previous studies would do, this study proposed a novel, data mining approach to address the problem. Specifically, the modified newsvendor problem were transformed into a classification problem. According to a set of relevant attributes, we used the regularized multiple criteria linear programming (RMCLP) model to classify dealers into two categories, namely Type A and Type B, according to the associated order quantity of dealers. Experiments showed that the RMCLP model gave high accuracy in predicting to which type a dealer belong to. One important implication of this study is to provide insights into the design and development of supply chain coordination policy.

Regularized multiple-criteria linear programming with universum and its application

Regularized multiple-criteria linear programming (RMCLP) model is a new powerful method for classification and has been used in various real-life data mining problems. In this paper, a new Universum-regularized multiple-criteria linear programming (called \({\mathfrak{U}}\)-RMCLP) was proposed and firstly applied to railway safety field, which is useful extension of RMCLP. Experiments in public datasets show that \({\mathfrak{U}}\)-RMCLP can get better results than its original model. Furthermore, experiment results in the trouble of moving freight car detection system (TFDS) datasets indicate that the accuracy of \({\mathfrak{U}}\)-RMCLP has been up to 91 %, which will provide great help for TFDS system.

Multi-instance classification based on regularized multiple criteria linear programming

Regularized multiple criteria linear programming (RMCLP) model is a new powerful method for classification and has been used in various real-life data mining problems. In this paper, a new Multi-instance Classification method based on RMCLP was proposed (called MI-RMCLP), which includes two algorithms for linearly separable case and nonlinearly case separately. The key point of this method, instead of a mixed integer quadratic programming in MI-SVM, is that it is able to deal with multi-instance learning problem by an iterative strategy solving sequential quadratic programming problems. All experiment results have shown that MI-RMCLP method can converge to the optimal value in limited iterative steps and be a competitive method in multi-instance learning classification.

Regularized Multiple Criteria Linear Programming via Linear Programming

Regularized multiple-criteria linear programming (RMCLP) model is a new powerful method for classification and has been used in various real-life data mining problems. In this paper, instead of solving quadratic programming (QP) in algorithm RMCLP, we proposed a novel RMCLP via linear programming (LP) for pattern classification(called LP-RMCLP). Numerical experiments on benchmark datasets show that the proposed method is comparable to traditional RMCLP in accuracy, however considerably faster than it.

A FUZZY LINEAR PROGRAMMING-BASED CLASSIFICATION METHOD

Multiple criteria linear programming and multiple criteria quadratic programming classification models have been applied in some field in financial risk analysis and credit risk control such as credit cardholders' behavior analysis. In this paper, a fuzzy linear programming classification method with soft constraints and criteria was proposed based on the previous findings from other researchers. In this method, the satisfied result can be obtained through selecting constraint and criteria boundary variable di*, respectively. A general framework of this method is also constructed. Two real-life datasets, one from a major USA bank and the other from a database of KDD 99, are used to test the accurate rate of the proposed method. And the result shows the feasibility of this method.

Predicting Auditor Changes Using Financial Distress Variables And The Multiple Criteria Linear Programming (MCLP) And Other Data Mining Approaches

<span style="font-family: Times New Roman; font-size: small;"> </span><h1 style="margin: 0in 0.5in 0pt; text-align: justify; page-break-after: auto; mso-pagination: none;"><span style="font-family: Times New Roman;"><span style="color: black; font-size: 10pt; mso-themecolor: text1;">Our study evaluates a multiple criteria linear programming (MCLP) </span><span style="color: black; font-size: 10pt; mso-themecolor: text1; mso-fareast-language: KO;">and other </span><span style="color: black; font-size: 10pt; mso-themecolor: text1;">data mining approach</span><span style="color: black; font-size: 10pt; mso-themecolor: text1; mso-fareast-language: KO;">es</span><span style="color: black; font-size: 10pt; mso-themecolor: text1;"> </span><span style="color: black; font-size: 10pt; mso-themecolor: text1; mso-fareast-language: KO;">to predict auditor changes using a portfolio of financial statement measures to capture financial distress</span><span style="color: black; font-size: 10pt; mso-themecolor: text1;">.<span style="mso-spacerun: yes;"> </span>The results of the MCLP approach and the other data mining approaches show that these methods perform</span><span style="color: black; font-size: 10pt; mso-themecolor: text1; mso-fareast-language: KO;"> reasonably well to predict auditor changes </span><span style="color: black; font-size: 10pt; mso-themecolor: text1;">using financial distress variables.</span><span style="color: black; font-size: 10pt; mso-themecolor: text1; mso-fareast-language: KO;"><span style="mso-spacerun: yes;"> </span>Overall accuracy rates are more than 60 percent, and true positive rates exceed 80 percent.<span style="mso-spacerun: yes;"> </span>Our study is designed to establish a starting point for auditor-change prediction using financial distress variables.<span style="mso-spacerun: yes;"> </span>Further research should incorporate additional explanatory variables and a longer study period to improve prediction rates.</span></span></h1><span style="font-family: Times New Roman; font-size: small;"> </span>

Decision Rule Extraction for Regularized Multiple Criteria Linear Programming Model

Due to the flexibility of multi-criteria optimization, Regularized Multiple Criteria Linear Programming (RMCLP) has received attention in decision support systems. Numerous theoretical and empirical studies have demonstrated that RMCLP is effective and efficient in classifying large scale data sets. However, a possible limitation of RMCLP is poor interpretability and low comprehensibility for end users and experts. This deficiency has limited RMCLP’s use in many real-world applications where both accuracy and transparency of decision making are required, such as in Customer Relationship Management (CRM) and Credit Card Portfolio Management. In this paper, the authors present a clustering based rule extraction method to extract explainable and understandable rules from the RMCLP model. Experiments on both synthetic and real world data sets demonstrate that this rule extraction method can effectively extract explicit decision rules from RMCLP with only a small compromise in performance.

Bankruptcy prediction for Korean firms after the 1997 financial crisis: using a multiple criteria linear programming data mining approach

The main purpose of this paper is to evaluate the data mining applications, such as classification, which have been used in previous bankruptcy prediction studies and credit rating studies. Our study proposes a multiple criteria linear programming (MCLP) method to predict bankruptcy using Korean bankruptcy data after the 1997 financial crisis. The results, of the MCLP approach in our Korean bankruptcy prediction study, show that our method performs as well as traditional multiple discriminant analysis or logit analysis using only financial data. In addition, our model’s overall prediction accuracy is comparable to those of decision tree or support vector machine approaches. However, our results are not generalizable because our data are from a special situation in Korea.