Global optimization of case-based reasoning for breast cytology diagnosis

Case-based reasoning (CBR) is one of the most popular prediction techniques in medical domains because it is easy to apply, has no possibility of overfitting, and provides a good explanation for the output. However, it has a critical limitation – its prediction performance is generally lower than other AI techniques like artificial neural networks (ANN). In order to obtain accurate results from CBR, effective retrieval and matching of useful prior cases for the problem is essential, but it is still a controversial issue to design a good matching and retrieval mechanism for CBR systems. In this study, we propose a novel approach to enhance the prediction performance of CBR. Our suggestion is the simultaneous optimization of feature weights, instance selection, and the number of neighbors that combine using genetic algorithms (GA). Our model improves the prediction performance in three ways – (1) measuring similarity between cases more accurately by considering relative importance of each feature, (2) eliminating useless or erroneous reference cases, and (3) combining several similar cases represent significant patterns. To validate the usefulness of our model, this study applied it to a real-world case for evaluating cytological features derived directly from a digital scan of breast fine needle aspirate (FNA) slides. Experimental results showed that the prediction accuracy of conventional CBR may be improved significantly by using our model. We also found that our proposed model outperformed all the other optimized models for CBR using GA.     

Bankruptcy prediction modeling with hybrid case-based reasoning and genetic algorithms approach

One of the most important research issues in finance is building effective corporate bankruptcy prediction models because they are essential for the risk management of financial institutions. Researchers have applied various data-driven approaches to enhance prediction performance including statistical and artificial intelligence techniques, and many of them have been proved to be useful. Case-based reasoning (CBR) is one of the most popular data-driven approaches because it is easy to apply, has no possibility of overfitting, and provides good explanation for the output. However, it has a critical limitation—its prediction performance is generally low. In this study, we propose a novel approach to enhance the prediction performance of CBR for the prediction of corporate bankruptcies. Our suggestion is the simultaneous optimization of feature weighting and the instance selection for CBR by using genetic algorithms (GAs). Our model can improve the prediction performance by referencing more relevant cases and eliminating noises. We apply our model to a real-world case. Experimental results show that the prediction accuracy of conventional CBR may be improved significantly by using our model. Our study suggests ways for financial institutions to build a bankruptcy prediction model which produces accurate results as well as good explanations for these results.     

多维优化案例推理检索算法研究

案例检索是案例推理系统的中心环节,检索质量关系着整个系统的质量。利用遗传算法GA和层次分析法AHP相结合,从案例库,属性的约简,权值确定三方面对案例检索进行优化。利用遗传算法在搜索优化上的优势,使用两维的编码结合权值从而形成三维优化,并利用经验和权值中间表进行权值学习。从而提高检索命中率。并将这种模型运用到基于旅游的多策略数据挖掘系统进行实验,结果表明在案例检索的命中率上有明显提高。     

A case-based reasoning system with the two-dimensional reduction technique for customer classification

Many studies have tried to optimize parameters of case-based reasoning (CBR) systems. Among them, selection of appropriate features to measure similarity between the input and stored cases more precisely, and selection of appropriate instances to eliminate noises which distort prediction have been popular. However, these approaches have been applied independently although their simultaneous optimization may improve the prediction performance synergetically. This study proposes a case-based reasoning system with the two-dimensional reduction technique. In this study, vertical and horizontal dimensions of the research data are reduced through our research model, the hybrid feature and instance selection process using genetic algorithms. We apply the proposed model to a case involving real-world customer classification which predicts customers’ buying behavior for a specific product using their demographic characteristics. Experimental results show that the proposed technique may improve the classification accuracy and outperform various optimized models of the typical CBR system.     

Forecasting of manufacturing cost in mobile phone products by case-based reasoning and artificial neural network models

The mobile phone manufacturers in Taiwan have made great efforts in proposing the rational quotations to the international phone companies with the ambition to win the bids by out beating other phone manufacturers. However, there are a lot of uncertainties and issues to be resolved in estimating the manufacturing costs for mobile phone manufacturers. As far as we know, there is no existing model which can be applied directly in forecasting the manufacturing costs. This research makes the first attempt to develop a hybrid system by integrated Case-Based Reasoning (CBR) and Artificial Neural Networks (ANN) as a Product Unit Cost (PUC) forecasting model for Mobile Phone Company. According to the cost formula of the mobile phone and experts’ opinions, a set of qualitative and quantitative factors are analyzed and determined. Qualitative factors are applied in CBR to retrieve a similar case from the case bases for a new phone product and ANN is used to find the relationship between the quantitative factors and the predicted PUC. Finally, intensive experiments are conducted to test the effectiveness of six different forecasting models. The model proposed in this research is compared with the other five models and the MAPE value of the proposed model is the smallest. This research provides a new prediction model with high accuracy for mobile phone manufacturing companies.     

A case-based classifier for hypertension detection

The exploration of three-dimensional (3D) anthropometry scanning data along with other existing subject medical profiles using data mining techniques becomes an important research issue for medical decision support. This research attempts to construct a classification approach based on the hybrid use of case-based reasoning (CBR) and genetic algorithms (GAs) for hypertension detection using anthropometric body surface scanning data. The obtained result reveals the relationship between a subject’s 3D scanning data and hypertension disease. The GA is adopted to determine the appropriate feature weights for CBR. The proposed approaches were experimented and compared with a regular CBR and other widely used approaches including neural nets and decision trees. The experiment showed that applying GA to determine the suitable weights in CBR is a feasible approach to improving the effectiveness of case matching of hypertension disease. It also demonstrated that different weighted CBR approach presents better classification accuracy over the results obtained from other approaches.     

Neural-based electricity load forecasting using hybrid of GA and ACO for feature selection

Due to deregulation of electricity industry, accurate load forecasting and predicting the future electricity demand play an important role in the regional and national power system strategy management. Electricity load forecasting is a challenging task because electric load has complex and nonlinear relationships with several factors. In this paper, two hybrid models are developed for short-term load forecasting (STLF). These models use “ant colony optimization (ACO)” and “combination of genetic algorithm (GA) and ACO (GA-ACO)” for feature selection and multi-layer perceptron (MLP) for hourly load prediction. Weather and climatic conditions, month, season, day of the week, and time of the day are considered as load-influencing factors in this study. Using load time-series of a regional power system, the performance of ACO?+?MLP and GA-ACO?+?MLP hybrid models is compared with principal component analysis (PCA)?+?MLP hybrid model and also with the case of no-feature selection (NFS) when using MLP and radial basis function (RBF) neural models. Experimental results and the performance comparison with similar recent researches in this field show that the proposed GA-ACO?+?MLP hybrid model performs better in load prediction of 24-h ahead in terms of mean absolute percentage error (MAPE).     

CASE-BASED REASONING FOR PREDICTING MULTIPERIOD FINANCIAL PERFORMANCES OF TECHNOLOGY-BASED SMEs

Recently, various types of technology funds became available to support the programs for technology development and commercialization of SMEs (Small and Medium Enterprise) in Korea. However, the potential financial performances have not been sufficiently considered at the selection stage of fund recipient SMEs whereas the default risk has been a major concern. This article proposes a Case Based Reasoning (CBR) system with Genetic Algorithm (GA) for predicting the Exponentially Weighted Moving Average (EWMA) of multiperiod financial performances of technology-oriented SMEs. It is expected that the proposed model can be applied to a wide range of technology investment-related decision-making procedures.     

Simultaneous optimization of artificial neural networks for financial forecasting

Artificial neural networks (ANNs) have been popularly applied for stock market prediction, since they offer superlative learning ability. However, they often result in inconsistent and unpredictable performance in the prediction of noisy financial data due to the problems of determining factors involved in design. Prior studies have suggested genetic algorithm (GA) to mitigate the problems, but most of them are designed to optimize only one or two architectural factors of ANN. With this background, the paper presents a global optimization approach of ANN to predict the stock price index. In this study, GA optimizes multiple architectural factors and feature transformations of ANN to relieve the limitations of the conventional backpropagation algorithm synergistically. Experiments show our proposed model outperforms conventional approaches in the prediction of the stock price index.     

基于CBR的电力负荷预测系统的研究与实现

随着电力系统的发展,负荷预测受到广泛重视。但由于它受到大量不确定因素的影响,导致电力负荷预测是一项重要而又非常复杂的工作,预测过程需要考虑多种因素。介绍了CBR的基本原理与方法,在分析相关技术的基础上,建立了一个基于CBR的电力负荷预测系统。实验分析结果表明该方法具有有效性和实用性,也说明了CBR在电力负荷预测系统的应用是提高电力系统生产规划、运行调度与管理水平,实现安全、高效和经济调度的重要技术手段。     

An application of a GA with Markov network surrogate to feature selection

Surrogate models of fitness have been presented as a way of reducing the number of fitness evaluations required by evolutionary algorithms. This is of particular interest with expensive fitness functions where the time taken for building the model is outweighed by the savings of using fewer function evaluations. In this article, we show how a Markov network model can be used as a surrogate fitness function for a genetic algorithm in a new algorithm called Markov Fitness Model Genetic Algorithm (MFM-GA). We thoroughly investigate its application to a fitness function for feature selection in Case-Based Reasoning (CBR), using a range of standard benchmarks from the CBR community. This fitness function requires considerable computation time to evaluate and we show that using the surrogate offers a significant decrease in total run-time compared to a GA using the true fitness function. This comes at the cost of a reduction in the global best fitness found. We demonstrate that the quality of the solutions obtained by MFM-GA improves significantly with model rebuilding. Comparisons with a classic GA, a GA using fitness inheritance and a selection of filter selection methods for CBR shows that MFM-GA provides a good trade-off between fitness quality and run-time.     

A hierarchical design of case-based reasoning in the balanced scorecard application

A balanced scorecard (BSC) is a management decision tool intended to be the corporate performance measurement. It also can play an important role in transforming an organization’s mission and strategy into a balanced set of integrated performance measures. Assigning suitable weight to each level of balanced scorecard is crucial to conduct performance evaluation effectively.In this research a case-based reasoning (CBR) system has been developed to assist in assigning the suitable weights. Based on the balanced scorecard design, this study proposed a three-level feature weights design to enhance CBR’s inference performance. For effective case retrieval, a genetic algorithm (GA) mechanism is employed to facilitate weighting all of levels in balanced scorecard and to determine the most appropriate three-level feature weights. The proposed approach is compared with the equal weights approach and the analytical hierarchy process (AHP) approach. The results indicate that the GA-CBR approach is able to produce more effective performance measurement.     

基于案例推理的金融危机预警支持系统

传统方法与模型预测金融危机有较大的局限性,该文提出用基于案例推理方法预测金融危机的思想,并给出基于案例推理的金融危机预警系统 ＣＢＲＦＣＰＳＳ的原型,研究了 ＣＢＲＦＣＰＳＳ中的关键技术：案例的知识表达、案例检索和案例学习等。文章最后给出了应用原型系统进行金融危机预警的部分研究成果。     

Hybridizing principles of TOPSIS with case-based reasoning for business failure prediction

Case-based reasoning (CBR) solves many real-world problems under the assumption that similar observations have similar outputs. As an implementation of this assumption and inspired by the technique for order performance by the similarity to ideal solution (TOPSIS), this paper proposes a new type of multiple criteria CBR method for binary business failure prediction (BFP) with similarities to positive and negative ideal cases (SPNIC). Assuming that the binary prediction of business failure generates two results, i.e., failure and non-failure, we set the principle of this CBR forecasting method which is termed as SPNIC-based CBR as follows: new observations should have the same output as the positive or negative ideal case to which they are more similar. From the perspective of CBR, the SPNIC-based CBR forecasting method consists of R⁴ processes: retrieving positive and negative ideal cases, reusing solutions of ideal cases to forecast, retain cases, and reconstruct the case base. As a demonstration, we applied this method to forecast business failure in China with three data representations of a formerly collected dataset from normal economic environment and a representation of a recently collected dataset from financial crisis environment. The results indicate that this new CBR forecasting method can produce significantly better short-term discriminate capability than comparative methods, except for support vector machine, in normal economic environment; On the contrary, it cannot produce acceptable performance in financial crisis environment. Further topics about this method are discussed.     

Hybrid genetic algorithms and case-based reasoning systems for customer classification

Abstract: Because of its convenience and strength in complex problem solving, case-based reasoning (CBR) has been widely used in various areas. One of these areas is customer classification, which classifies customers into either purchasing or non-purchasing groups. Nonetheless, compared to other machine learning techniques, CBR has been criticized because of its low prediction accuracy. Generally, in order to obtain successful results from CBR, effective retrieval of useful prior cases for the given problem is essential. However, designing a good matching and retrieval mechanism for CBR systems is still a controversial research issue. Most previous studies have tried to optimize the weights of the features or the selection process of appropriate instances. But these approaches have been performed independently until now. Simultaneous optimization of these components may lead to better performance than naive models. In particular, there have been few attempts to simultaneously optimize the weights of the features and the selection of instances for CBR. Here we suggest a simultaneous optimization model of these components using a genetic algorithm. To validate the usefulness of our approach, we apply it to two real-world cases for customer classification. Experimental results show that simultaneously optimized CBR may improve the classification accuracy and outperform various optimized models of CBR as well as other classification models including logistic regression, multiple discriminant analysis, artificial neural networks and support vector machines.     

GA和SVM在供应链绩效评价中的应用

研究了基于遗传算法和支持向量机的供应链绩效评价问题。将供应链绩效评价问题用遗传算法进行特征选择并同时对支持向量机参数进行了优化。研究表明该方法能提取出影响供应链绩效的重要属性,减小供应链评价模型的复杂度。应用实例表明基于遗传算法和支持向量机的评价结果从整体上要优于标准支持向量机的评价结果。     

案例系统特征权值优化算法的仿真研究

研究案例系统特征权值优化问题,传统特征权值确定方法过分依赖主观判断和经验,而单一遗传算法或禁忌算法存在各自的不足,因此案例分类精度低。为了提高案例分类精度,提出一种遗传算法和禁忌算法相融合的案例系统特征权值优化方法。利用遗传算法全局搜索能力、并行性和禁忌算法局部搜索和记忆能力,有效地解决了案例系统特征权值优化问题。仿真结果表明,混合方法利用了遗传算法和禁忌算法的优点,很好地优化了案例系统特征权值,从而加快案例系统检索速度,提高了案例分类精度。     

基于IGA的支持向量机特征子集选择和参数优化

特征子集选择和训练参数的优化一直是SVM研究中的两个重要方面,选择合适的特征和合理的训练参数可以提高SVM分类器的性能,以往的研究是将两个问题分别进行解决。随着遗传优化等自然计算技术在人工智能领域的应用,开始出现特征选择及参数的同时优化研究。研究采用免疫遗传算法（IGA）对特征选择及SVM 参数的同时优化,提出了一种IGA-SVM 算法。实验表明,该方法可找出合适的特征子集及SVM 参数,并取得较好的分类效果,证明算法的有效性。     

A computerized causal forecasting system using genetic algorithms in supply chain management

Forecasting activities are widely performed in the various areas of supply chains for predicting important supply chain management (SCM) measurements such as demand volume in order management, product quality in manufacturing processes, capacity usage in production management, traffic costs in transportation management, and so on. This paper presents a computerized system for implementing the forecasting activities required in SCM. For building a generic forecasting model applicable to SCM, a linear causal forecasting model is proposed and its coefficients are efficiently determined using the proposed genetic algorithms (GA), canonical GA and guided GA (GGA). Compared to canonical GA, GGA adopts a fitness function with penalty operators and uses population diversity index (PDI) to overcome premature convergence of the algorithm. The results obtained from two case studies show that the proposed GGA provides the best forecasting accuracy and greatly outperforms the regression analysis and canonical GA methods. A computerized system was developed to implement the forecasting functions and is successfully running in real glass manufacturing lines.