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Multiple classifier systems have been used widely in computing, communications, and informatics. Combining multiple classifier systems (MCS) has been shown to outperform a single classifier system. It has been demonstrated that improvement in ensemble performance depends on either the diversity among or the performance of individual systems. A variety of diversity measures and ensemble methods have been proposed and studied. However, it remains a challenging problem to estimate the ensemble performance in terms of the performance of and the diversity among individual systems. The purpose of this paper is to study the general problem of estimating ensemble performance for various combination methods using the concept of a performance distribution pattern (PDP).

In particular, the paper establishes upper and lower bounds for majority voting ensemble performance with disagreement diversity measure Dis, weighted majority voting performance in terms of weighted average performance and weighted disagreement diversity, and plurality voting ensemble performance with entropy diversity measure D.

Bounds for these three cases are shown to be tight using the PDP for the input set.

As a consequence of the authors' previous results on diversity equivalence, the results of majority voting ensemble performance can be extended to several other diversity measures. Moreover, the paper showed in the case of majority voting ensemble performance that when the average of individual systems performance P is big enough, the ensemble performance P^m resulting from a maximum (information‐theoretic) entropy PDP is an increasing function with respect to the disagreement diversity Dis. Eight experiments using data sets from various application domains are conducted to demonstrate the complexity, richness, and diverseness of the problem in estimating the ensemble performance.

Customer Support Knowledge of Customer Support Organization is one of the important assets of enterprises and “Customer Support Knowledge Management” is also the critical aspect of Business Knowledge Management; however, the attributes of Customer Support Knowledge are complicated, diverse, renewed rapidly and difficult to be managed. Thus, in order to design a successful Customer Support Knowledge Management System, apart from the consideration of “human” and “information technology” aspects, the concerns of attributes and Customer Support Knowledge and industry characteristics should be involved for meeting the requirements of Customer Support Organization and allowing the organization to acquire the competitive advantage of “Differentiation Service”. This research used the “Customer Support Knowledge Management System” in a high‐tech industry as an example and treated the end users of medical instruments in different types of hospitals in Taiwan which have received the support service of our company in recent six months as the population. The end users were mostly the nursing executives or ultrasonic wave technical personnel in intensive care unit and they had similar educational background and incomes and adopted the medical instruments such as physical supervision system, ultrasonic wave system, heart start or ECG machine produced by our company; the research method was to randomly treat the investigation results of the telephone customers’ satisfaction from respective 30 end users in the population three months before and after this system execution as the samples and use hypotheses to validate if the end users’ customer satisfaction significantly improved in terms of “Remote Support,” “On‐site Support,” “Service Turn Around time,” “Technical Competence” and “Service Manner” in order to understand the influence and managerial significance of execution of “Customer Support Knowledge Management System” on Customer Support Organization.