Pei-Lun Hsu

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 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.

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

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.

A Constraint-Based Optimization Mechanism for Patient Satisfaction

Patient satisfaction is a critical criterion for healthcare Customer Relationship Management (CRM). By retrieving and adapting previous similar patient cases, our previous research proposed a case-based prediction model to predict patient satisfaction [3]. The prediction model is useful to forecast the possible patient satisfaction level for a target patient segment. Based on the prediction model, this research further proposes a constraint-based optimization mechanism to determine the optimum values of case features that best approximate the goal of patient satisfaction. The optimization model can help healthcare providers develop appropriate CRM plans to upgrade the satisfaction level for a target patient segment. Two hundred eighty-four real patient cases are collected in the case base for the experiment. The integrated system with both prediction and optimization models can support the decision making of healthcare providers to establish a proactive CRM as well as provide better healthcare services.

Regression trees approach for flow-time prediction in wafer manufacturing processes using constraint-based genetic algorithm

Understanding the factors associated with the flow-time of wafer production is crucial for workflow design and analysis in wafer fabrication factories. Owing to wafer fabrication complexity, the traditional human approach to assigning the due-date is imprecise and prone to failure, especially when the shop status is dynamically changing. Therefore, assigning a due-date to each customer order becomes a challenge to production planning. The paper proposes a constraint-based genetic algorithm approach to determine the flow-time. The flow-time prediction model was constructed and compared with other approaches. Better computational effectiveness and prediction results from the constraint-based genetic algorithm are demonstrated using experimental data from a wafer-manufacturing factory.

Combining apriori algorithm and constraint-based genetic algorithm for tree induction for aircraft electronic ballasts troubleshooting

Reliable and effective maintenance support is vital to the airline operations and flight safety. This research proposes the hybrid of apriori algorithm and constraint-based genetic algorithm (ACBGA) approach to discover a classification tree for electronic ballasts troubleshooting. Compared with a simple GA (SGA) and the Apriori algorithms with GA (AGA), the ACBGA achieves higher classification accuracy for electronic ballast data.

Customer Relationship Management in Healthcare Service – An Integrated DSS Framework for Patient Loyalty

Patient loyalty is a critical criterion for healthcare customer relationship management (CRM). An integrated framework with a case-based prediction model and a constraint-based optimization model is proposed to support the decision making of healthcare providers. This research first adopts a case-based prediction mechanism to forecast the possible loyalty level. We also proposes a constraint-based optimization approach as a subsequent mechanism to determine the optimum values of case features that may lead to the optimal patient loyalty. The potential use of this framework helps a decision maker allocate resources to increase the loyalty level for the given target patient segmentation.