The Wisdom of Crowds, an innovative theory described in social science, claims that the aggregate decisions made by a group will often be better than those of its individual members if the four fundamental criteria of this theory are satisfied. This theory used for in clustering problems. Previous researches showed that this theory can significantly increase the stability and performance of learning problems. As a solution, this paper proposes a new methodology of using WOC theory for evaluating and selecting basic result partitions in semi-supervised clustering problems. This paper introduces new technique for reducing the data dimensions based on supervision information, a new semi-supervised clustering algorithm based on k-means for generating basic results, a new strategy for evaluating and selecting basic results based on feedback mechanism, a new metric for evaluating diversity of basic results. The results demonstrate the efficiency of proposed method's aggregate decision-making compared to other algorithms. تفاصيل المقالة

Classification Ensemble, which uses the weighed polling of outputs, is the art of combining a set of basic classifiers for generating high-performance, robust and more stable results. This study aims to improve the results of identifying the Persian handwritten letters using Error Correcting Output Coding (ECOC) ensemble method. Furthermore, the feature selection is used to reduce the costs of errors in our proposed method. ECOC is a method for decomposing a multi-way classification problem into many binary classification tasks; and then combining the results of the subtasks into a hypothesized solution to the original problem. Firstly, the image features are extracted by Principal Components Analysis (PCA). After that, ECOC is used for identification the Persian handwritten letters which ituses Support Vector Machine (SVM) as the base classifier. The empirical results of applying this ensemble method using 10 real-world data sets of Persian handwritten letters indicate that this method has better results in identifying the Persian handwritten letters than other ensemble methods and also single classifications. Moreover, by testing a number of different features, this paper found that we can reduce the additional cost in feature selection stage by using this method. تفاصيل المقالة