Chaochang Chiu

A case-based customer classification approach for direct marketing

Abstract Case-based reasoning (CBR) shows significant promise for improving the effectiveness of complex and unstructured decision making. CBR is both a paradigm for computer-based problem-solvers and a model of human cognition. However the design of appropriate case retrieval mechanisms is still challenging. This paper presents a genetic algorithm (GA)-based approach to enhance the case-matching process. A prototype GA–CBR system used to predict customer purchasing behavior is developed and tested with real cases provided by one worldwide insurance direct marketing company, Taiwan branch. The results demonstrate better prediction accuracy over the results from the regression-based CBR system. Also an optimization mechanism is integrated into the classification system to reveal those customers most likely and most unlikely customers to purchase insurance.

Predicting information systems outsourcing success using a hierarchical design of case-based reasoning

Abstract In recent years, much attention has been focused on information systems (IS) outsourcing by practitioners as well as academics. However, our understanding of the factors influencing IS outsourcing success is still incomplete because of lacking empirical results. The attempt to forecast IS outsourcing success using the affecting factors thus becomes challenging. Case-based Reasoning (CBR) is a machine reasoning method that adapts previous similar cases to infer further similarity. CBR method is adopted to analogize IS attributes to the consequences of IS outsourcing practices. This study proposed a two-level feature weights design to enhance CBRs inferencing performance. For effective case retrieval, a Genetic Algorithm mechanism is employed to determine the most appropriate two-level feature weights. One hundred and forty-six real IS outsourcing cases, each with 22 features and eight outcome features are collected as the case base. The proposed approach is compared with the equal weights approach and the regression method. The results indicate that our approach is able to produce more effective prediction outcomes.

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 organizations 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 CBRs 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.

A constraint-based genetic algorithm approach for mining classification rules

Data mining is an information extraction process that aims to discover valuable knowledge in databases. Existing genetic algorithms (GAs) designed for rule induction evaluates the rules as a whole via a fitness function. Major drawbacks of GAs for rule induction include computation inefficiency, accuracy and rule expressiveness. In this paper, we propose a constraint-based genetic algorithm (CBGA) approach to reveal more accurate and significant classification rules. This approach allows constraints to be specified as relationships among attributes according to predefined requirements, users preferences, or partial knowledge in the form of a constraint network. The constraint-based reasoning is employed to produce valid chromosomes using constraint propagation to ensure the genes to comply with the predefined constraint network. The proposed approach is compared with a regular GA and C4.5 using two UCI repository data sets. Better classification accurate rates from CBGA are demonstrated.

The hybrid of association rule algorithms and genetic algorithms for tree induction: an example of predicting the student course performance

Abstract Revealing valuable knowledge hidden in corporate data becomes more critical for enterprise decision making. When more data is collected and accumulated, extensive data analysis would not be easier without effective and efficient data mining methods. This paper proposes a hybrid of the association rule algorithm and genetic algorithms (GAs) approach to discover a classification tree. The association rule algorithm is adopted to obtain useful clues based on which the GA is able to proceed its searching tasks in a more efficient way. In addition an association rule algorithm is employed to acquire the insights for those input variables most associated with the outcome variable before executing the evolutionary process. These derived insights are converted into GAs seeding chromosomes. The proposed approach is experimented and compared with a regular genetic algorithm in predicting a students course performance.

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 subjects 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.

Mining Three-Dimensional Anthropometric Body Surface Scanning Data for Hypertension Detection

Hypertension is a major disease, being one of the top ten causes of death in Taiwan. The exploration of three-dimensional (3-D) 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 prediction model for hypertension using anthropometric body surface scanning data. This research adopts classification trees to reveal the relationship between a subjects 3-D scanning data and hypertension disease using the hybrid of the association rule algorithm (ARA) and genetic algorithms (GAs) approach. The ARA is adopted to obtain useful clues based on which the GA is able to proceed its searching tasks in a more efficient way. The proposed approach was experimented and compared with a regular genetic algorithm in predicting a subjects hypertension disease. Better computational efficiency and more accurate prediction results from the proposed approach are demonstrated

Opinion mining of hotel customer-generated contents in Chinese weblogs

Customer-generated contents in weblogs provide tourism organisations with valuable market intelligence and ongoing market research opportunities. In this study, an opinion mining method based on feature-based sentiment classification is proposed to extract the online electronic word-of-mouth on weblogs in Taiwan. For opinion extraction, a supervised semantic orientation using the point-wise mutual information (SO_PMI) algorithm based on the extension of Turneys unsupervised SO_PMI algorithm is proposed to extract the opinion words. In addition, a heuristic n-phrase rule is proposed to find out customer opinions about hotel attributes, including hotel image, services, price/value, food and beverage, room, amenities, and location. The experimental results show that the proposed approach mixed with supervised SO_PMI algorithm and heuristic n-phrase rule can demonstrate its effectiveness with acceptable classification and forecasting performances. Furthermore, a perceptual map based on correspondence analysis visually presents opinions comparison to provide the insight of competitive positions.

An adapted covering algorithm approach for modeling airplanes landing gravities

Abstract The covering algorithm is an often-used rule induction method. The main shortcoming of this algorithm is its inability to handle continuous-type data. The present research proposes a novel method that integrates genetic algorithms with covering algorithms in support of rule induction dealing with both continuous and categorical data types. We illustrate this method and demonstrate its effectiveness with data obtained directly from the flight data recorders of Boeing 747-400 airplanes. The results indicate that the adapted covering algorithm is feasible as a complete rule induction method.

An intelligent support system for supplier evaluation

Supplier evaluation is critical for Supply Chain Management (SCM). Based on the information gained from formalised supplier evaluation processes, a company can determine the relationship with the suppliers. If manufacturers can cooperate or interact with suppliers very well, it helps to maximise the productivity at the minimum cost while satisfying customer requirements. Case-Based Reasoning (CBR) provides a suitable mean for improving the effectiveness of complex and unstructured decision making, such as supplier evaluation. It is a machine reasoning method that adapts previous similar cases to infer further similarity. For effective case retrieval, a Genetic Algorithm (GA)-based approach used to determine the most appropriate feature weights is employed to enhance the case-matching process. Our proposed GA-CBR methodology used to predict vendor evaluation has been proven very effective and has better prediction accuracy over the results from other approaches as it minimises the effect of qualitative factors.