One of the crucial stages in machine learning in high-dimensional datasets is feature selection. Unrelated features weaknesses the efficiency of the model. However, merging several feature selection strategies is routine to solve this problem, the way to integrate feature selection methods is problematic. This paper presents a new ensemble of heuristics through fuzzy Type-I based on Ant Colony Optimization (ACO) for ensemble feature selection named Ant-EHFS. At first, three feature selection methods are run; then, the Euclidean Distance between each pair of features is computed as a heuristic (an M×M matrix is constructed), that M is the total of features. After that, a Type-I fuzzy is used individually to address various feature selections' uncertainty and estimate trustworthiness for each feature, as another heuristic. A complete weighted graph based on combining the two heuristics is then built; finally, ACO is applied to the complete graph for finding features that have the highest relevance together in the features space, which in each ant considers the reliability rate and Euclidean Distance of the destination node together for moving between nodes of the graph. Five and eight robust and well-known ensemble feature selection methods and primary feature selection methods, respectively, have been compared with Ant-EHFS on six high-dimensional datasets to show the proposed method's performance. The results have shown that the proposed method outperforms five ensemble feature selection methods and eight primary feature selections in Accuracy, Precision, Recall, and F1-score metrics.

Clustering is the process of division of a dataset into subsets that are called clusters, so that objects within a cluster are similar to each other and different from objects of the other clusters. So far, a lot of algorithms in different approaches have been created for the clustering. An effective choice (can combine) two or more of these algorithms for solving the clustering problem. ensemble clustering combines results of existing clusterings to achieve better performance and higher accuracy. Instead of combining all of existing clusterings, recent decade researchers show, if only a set of clusterings is selected based on quality and diversity, the result of ensemble clustering would be more accurate. This paper proposes a new method for ensemble clustering based on quality and diversity. For this purpose, firstly first we need a lot of different base clusterings to combine them. Different base clusterings are generated by k-means algorithm with random k in each execution. After the generation of base clusterings, they are put into different groups according to their similarities using a new grouping method. So that clusterings which are similar to each other are put together in one group. In this step, we use normalized mutual information (NMI) or adjusted rand index (ARI) for computing similarities and dissimilarities between the base clustering. Then from each group, a best qualified clustering is selected via a voting based method. In this method, Cluster-validity-indices were used to measure the quality of clustering. So that all members of the group are evaluated by the Cluster-validity-indices. In each group, clustering that optimizes the most number of Cluster-validity-indices is selected. Finally, consensus functions combine all selected clustering. Consensus function is an algorithm for combining existing clusterings to produce final clusters. In this paper, three consensus functions including CSPA, MCLA, and HGPA have used for combining clustering. To evaluate proposed method, real datasets from UCI repository have used. In experiment section, the proposed method is compared with the well-known and powerful existing methods. Experimental results demonstrate that proposed algorithm has better performance and higher accuracy than previous works.

Crowdfunding is a new technology-enabled innovative process that is changing the capital market space. Internet-based applications, particularly those related to Web 2. 0, have had a significant impact on sectors of society such as education, business, and medicine. The goal of this research is to fill a gap in the literature on mathematical modelling and prediction of ensemble learning in order to evaluate crowdfunding projects. The Mathematical model determines the cost of funding for the entrepreneur and the return investors will receive per period. A correct financial model is essential in order to keep all three stakeholders involved in the long term. The results show the designed model improved performance in predicting the evaluation of success or failure of Crowdfunding projects.