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Heart disease classification is one of the most important topics in clinical decision support systems (CDSS). However, the performance of classification is greatly affected by feature selection. Canonical correlation analysis (CCA) is a popular method to extract effective features from two relevant data sets. In this paper, we employ discriminant minimum class locality preserving canonical correlation...
With the development of machine learning techniques, artificial intelligence applications in medicine are becoming hot topic in health information systems. In this research, we construct a new basic heart failure disease database which contains 1715 patients and 400 features. Then, we propose a new machine learning method called Polynomial Smooth Support Vector Machine(PSSVM) to help doctors diagnose...
By simulating the clustering behavior of the real-world ant colonies, we propose in this paper a constrained ant clustering algorithm based on random walk to deal with the constrained clustering problems with pairwise must-link and cannot-link constraints. Experimental results show that our approach is more effective on both synthetic datasets and UCI datasets compared with the cop-kmeans algorithm...
Nonlinear multi-classification has been a popular task in machine learning recently. In this paper, we propose a nonlinear multi-classification algorithm named Supervised Spectral Space Classifier (S3C), S3C integrates the discriminative information into the spectral graph mapping and transforms the input data into the low-dimensional supervised spectral space. S3C not only enables researchers to...
Intuition-based learning (IBL) has been used in various problem-solving areas such as risk analysis, medical diagnosis and criminal investigation. However, conventional IBL has the limitation that it has no criterion for choosing the trusted intuition based on the knowledge and experience. The purpose of this paper is to develop a learning model for human-computer cooperative from user's perspective...
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