A cost-sensitive learning algorithm for fuzzy rule-based classifiers

Designing classifiers may follow different goals. Which goal to prefer among others depends on the given cost situation and the class distribution. For example, a classifier designed for best accuracy in terms of misclassifica- tions may fail when the cost of misclassification of one class is much higher than that of the other. This paper presents a decision-theoretic extension to make fuzzy rule generation cost-sensitive. Furthermore, it will be shown how interpretability aspects and the costs of feature acquisition can be ac- counted for during classifier design. Natural language text is used to explain the generated fuzzy rules and their design proces

From Classification Rules to Action Recommendations

Rule induction has attracted a great deal of attention in Machine Learning and Data Mining. However, generating rules is not an end in itself because their applicability is not straightforward especially when the number of rules is large. Ideally, the user would ultimately like to use these rules to decide which actions to take. In the literature, this notion is usually referred to as actionability. The contribution of this paper1 is two-fold: first we propose a survey of the main approaches developed to address actionability. This topic has received growing attention in the past years. We present a classification of the main research in this area as well as a comparative study between the different approaches. Second, we propose a new framework to address actionability. Our goal is to lighten the burden of analyzing a large set of classification rules when the user is confronted with an "unsatisfactory situation" and needs help to decide what appropriate actions to take in order to remedy the situation. The method consists in comparing the situation to a set of classification rules. This is achieved by using a suitable distance that allows one to suggest action recommendations requiring minimal changes to improve the situation. We propose the algorithm DAKAR for learning action recommendations and we present an application to environment protection. Our experiment shows the usefulness of our contribution for action recommendation but also raises some concerns about the impact of the redundancy of a set of rules in learning action recommendations of good quality

Data mining in manufacturing: a review based on the kind of knowledge

In modern manufacturing environments, vast amounts of data are collected in database management systems and data warehouses from all involved areas, including product and process design, assembly, materials planning, quality control, scheduling, maintenance, fault detection etc. Data mining has emerged as an important tool for knowledge acquisition from the manufacturing databases. This paper reviews the literature dealing with knowledge discovery and data mining applications in the broad domain of manufacturing with a special emphasis on the type of functions to be performed on the data. The major data mining functions to be performed include characterization and description, association, classification, prediction, clustering and evolution analysis. The papers reviewed have therefore been categorized in these five categories. It has been shown that there is a rapid growth in the application of data mining in the context of manufacturing processes and enterprises in the last 3 years. This review reveals the progressive applications and existing gaps identified in the context of data mining in manufacturing. A novel text mining approach has also been used on the abstracts and keywords of 150 papers to identify the research gaps and find the linkages between knowledge area, knowledge type and the applied data mining tools and techniques

Customer focused optimal design skill training module from the informatics perspective

Design informatics refers to the processing and application of information in the design process. In the case study of Product-Service Bundle (PSB), design analysis has become challenging due to the increasing amount of complex design information. Given such a design complexity, there are challenges in term of technical and educational needs of data-driven design. Therefore, a design skill training module of customer-focused optimal design was proposed. This study was conducted based on four main parts: (a) Part I: The Customer Knowledge Discovery (conjoint analysis and decision tree method); (b) Part II: The PSB Design and Optimisation (multiobjective optimisation technique); (c) Part III: The Design and Development of Training Modules (ADDIE Model) with Experts Validation (n=5); and (d) Part IV: The User Study of Skill Training Module (n=21). As results, in Part I the generated rules for product-service that matching the product and service features were identified. Part II, two case studies that show new PSB pricing reference based on existing offers were illustrated. Then, four modules of customer-focused optimal design training were developed in Part III and the average of expert’s validation score, 70%-85% were obtained which exceeding the suggested acceptable threshold, 70%. Finally, an increment of trainees achievement that obtained ‘A’ grade in each training was recorded in Part IV; 23.81%, 28.57%, 38.10%, and 61.90%, respectively. Besides, the frequencies of trainee’s achievement grades were presented based on demographic profiles; (i) working experiences, with (n=3), without (n=13), and training (n=5); (ii) level of skills; basic (n=6), intermediate (n=12), advanced (n=3), respectively. Lastly, the feedback of post-training survey presented good usability rating and feasibility of the suggested training modules. In conclusion, this study provides one of the potential solutions for solving design issues that can be applied in engineering education

A Process Mining Based Approach to Complex Manufacturing Process Flow Analysis: A Case Study

Department of Management EngineeringWith recent advances in IT infrastructure in manufacturing environments, a large amount of manufacturing data are collected and stored in a database at various stages of production. These data may include valuable information for manufacturing companies to improve their manufacturing processes. The method of manufacturing data analysis is crucial for understanding the manufacturing data. However, traditional manufacturing data analysis methods such as data mining, simulation, etc. have limitations for this purpose since those are difficult to provide overall process-level information. Therefore, in this thesis, a process mining based approach for analyzing complex manufacturing processes is proposed. Process mining is a useful tool for process-related knowledge acquisition since it enables users to derive not only manufacturing process models, but also several performance measures related to processes, resources, and tasks. This thesis suggests a framework for the manufacturing process analysis. To do this, it applies process mining techniques to perform four types of analysis, which are visualization of production flows, machine-to-machine inter-relationship analysis, machine utilization, and monitoring & diagnosis of task performance regarding yield rate and lead time. Furthermore, a case study is conducted to support the proposed framework with an event log of an electronic components manufacturing process.ope

A Decision-Theoretic Approach to Data Mining

In this paper, we develop a decision-theoretic framework for evaluating data mining systems, which employ classification methods, in terms of their utility in decision-making. The decision-theoretic model provides an economic perspective on the value of â extracted knowledge,â in terms of its payoff to the organization, and suggests a wide range of decision problems that arise from this point of view. The relation between the quality of a data mining system and the amount of investment that the decision maker is willing to make is formalized. We propose two ways by which independent data mining systems can be combined and show that the combined data mining system can be used in the decision-making process of the organization to increase payoff. Examples are provided to illustrate the various concepts, and several ways by which the proposed framework can be extended are discussed