The past decade has seen a significant interest on the problem of inducing decision trees that take account of costs of misclassification and costs of acquiring the features used for decision making. This survey identifies over 50 algorithms including approaches that are direct adaptations of accuracy based methods, use genetic algorithms, use anytime methods and utilize boosting and bagging. The survey brings together these different studies and novel approaches to cost-sensitive decision tree learning, provides a useful taxonomy, a historical timeline of how the field has developed and should provide a useful reference point for future research in this field.

Abstract

Global competition and rapidly changing customer requirements are demanding increasing changes in manufacturing environments. Enterprises are required to constantly redesign their products and continuously reconfigure their manufacturing systems. Traditional approaches to manufacturing systems do not fully satisfy this new situation. Many authors have proposed that artificial intelligence (AI) will bring the flexibility and efficiency needed by manufacturing systems. This paper is a review of AI techniques used in manufacturing systems. The paper first defines the components of a simplified intelligent manufacturing systems (IMS), the different AI techniques to be considered and then shows how these AI techniques are used for the components of IMS.

Decision tree induction is a widely used technique for learning from data, which first emerged in the 1980s. In recent years, several authors have noted that in practice, accuracy alone is not adequate, and it has become increasingly important to take into consideration the cost of misclassifying the data. Several authors have developed techniques to induce cost-sensitive decision trees. There are many studies that include pair-wise comparisons of algorithms, but the comparison including many methods has not been conducted in earlier work. This paper aims to remedy this situation by investigating different cost-sensitive decision tree induction algorithms. A survey has identified 30 cost-sensitive decision tree algorithms, which can be organized into 10 categories. A representative sample of these algorithms has been implemented and an empirical evaluation has been carried. In addition, an accuracy-based look-ahead algorithm has been extended to a new cost-sensitive look-ahead algorithm and also evaluated. The main outcome of the evaluation is that an algorithm based on genetic algorithms, known as Inexpensive Classification with Expensive Tests, performed better over all the range of experiments thus showing that to make a decision tree cost-sensitive, it is better to include all the different types of costs, that is, cost of obtaining the data and misclassification costs, in the induction of the decision tree.

The last decade has seen a considerable growth in the use of AI for operations management with the aim of finding solutions to problems that are increasing in complexity and scale. This paper begins by setting the context for the survey through a historical perspective of OR and AI. An extensive survey of applications of AI techniques for operations management, covering a total of over 1200 papers published from 1995 to 2004 is then presented. The papers are categorised into four areas of operations management: (a) design, (b) scheduling, (c) process planning and control and (d) quality, maintenance and fault diagnosis. Each of the four areas is categorised in terms of the AI techniques used: genetic algorithms, case based reasoning, knowledge based systems, fuzzy logic and hybrid techniques. The trends over the last decade are identified, discussed with respect to expected trends and directions for future work suggested.

The validation of data from sensors has be come an important issue in the operation and control of modern industrial plants. One ap proach is to use know ledge based techniques to detect inconsistencies in measured data.This article presents a probabilistic model for the detection of such inconsistencies. Based on probability propagation, this method is able to find the existence of a possible fault among the set of sensors. That is, if an er ror exists, many sensors present an apparent fault due to the propagation from the sen:. sor(s) with a real fault. So the fault detection mechanism can only tell if a sensor has a po tentwl fault, but it can not tell if the fault is real or apparent. So the central problem is to develop a theory, and then an algorithm, for distinguishing real and apparent faults, given that one or more sensors can fail at the same time. This article then, presents an approach based on two levels: (i) probabilistic reason ing, to detect a potential fault, and (ii) con straint management, to distinguish the real fault from the apparent. ones. The proposed approach is exemplified by applying it to a power plant model.

No abstract

No abstract