user in interactive evolutionary computation (IEC) has the characteristic of fuzzy cognition. Based on this, a method to learn users’ fuzzy cognition knowledge is given. The method includes the fuzzy expression of the basic elements of IEC such as search space, population, gene sense unit and so on. Then a method to increase the performance of IEC based on the knowledge of users’ fuzzy cognition is given. The above results enrich the researches of IEC users' cognition.
The early warning system of College Students’ target course achievement is an important part of the educational administration system in Colleges and universities. This paper proposes to use some techniques of association principle to mine a large amount of data in the performance system to a certain extent, and obtain available rules from the data. Based on the characteristics and shortcomings of Apriori algorithm, an improved Apriori is proposed. The algorithm can process and mine the data in the early warning system of College Students’ scores, and finally obtain the management principles, thus forming an effective early warning for the course learning. In order to promote the improvement of students’ academic performance and achieve the ultimate goal of cultivating excellent talents in Colleges and universities.
A new cooperation of the enterprises and college model is presented in this paper. The new cooperation model not only avoids the dependent on the government severely, but also stimulates the passion of the enterprises in the cooperation between enterprises and college. The best features about the new cooperation model are leaded by the social need and employment of the multi-agents instead of the only college. The application results show that the educational quality has advanced obviously through the CNM cooperation between the enterprises and college based on social need.
This paper proposes an assembling classifier consisting of a global classifier and a local classifier, named as GCLC. To this end, we present a weighted Support Vector Machine (wSVM) that serves as the global classifier, and a fuzzy k-nearest neighbor (fkNN) that serves as the local one. When a query arrives, wSVM labels it firstly. If the global decision is below some threshold, the local fkNN works to provide an improved decision. Extensive experiments on real datasets demonstrate the performance of GCLC compared with the state of the art.
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