Outlier Detection Using Extreme Learning Machines Based on Quantum Fuzzy C-Means

One of the most important concerns of a data miner is always to have accurate and error-free data. Data that does not contain human errors and whose records are full and contain correct data. In this paper, a new learning model based on an extreme learning machine neural network is proposed for outlier detection. The function of neural networks depends on various parameters such as the structure of them, initial weights, number of hidden layer neurons, and learning rate. Quantum computing is a new method of information processing based on quantum mechanics, the concepts of which are also used today in applications of artificial intelligence. In the proposed method, the neural network of the extreme learning machine is improved using the concept of the quantum fuzzy clustering c-Means. This clustering helps to find the optimal weight of the input layer connections to the hidden layer of the neural network. It also allows network architecture to be constructively constructed in the hidden layer and improves learning. The performance of the proposed method in terms of accuracy, correct positive rate and correct negative rate shows the superiority of the proposed method in detecting outlier data compared to other methods.