0950-7051/$ see front matter 2011 Elsevier B.V. A doi:10.1016/j.knosys.2011.06.008 ⇑ Corresponding author at: Key Laboratory of Knowledge Engineering of Ministry of Education, 130012, China. E-mail addresses: [email protected], liudayou19420 Bankruptcy prediction is one of the most important issues in financial decision-making. Constructing effective corporate bankruptcy prediction models in tim...

K-nearest-neighbor query is an important query in uncertain network, which is finding the k close nodes to a specific node. We first put forward the concept of the credible nearest neighbor query in uncertain network, and give credible k-nearest-neighbor query algorithm. Credible distance is used to describe the distance between nodes in uncertain network. Fuzzy simulation is adopted to decreas...

Abstract The fuzzy k-nearest neighbor (FKNN) algorithm, one of the most well-known and effective supervised learning techniques, has often been used in data classification problems but rarely regression settings. This paper introduces a new, more general model. Generalization is based on usage Minkowski distance instead usual Euclidean distance. not optimal choice for practical problems, better...

Spam is an unwanted email that is harmful to communications around the world. Spam leads to a growing problem in a personal email, so it would be essential to detect it. Machine learning is very useful to solve this problem as it shows good results in order to learn all the requisite patterns for classification due to its adaptive existence. Nonetheless, in spam detection, there are a large num...

In this paper, we present an effective and efficient diagnosis system using fuzzy k-nearest neighbor (FKNN) for Parkinson’s disease (PD) diagnosis. The proposed FKNN-based system is compared with the support vector machines (SVM) based approaches. In order to further improve the diagnosis accuracy for detection of PD, the principle component analysis was employed to construct the most discrimin...

tizzy k nearest neighbor rule (k-NNR) has been applied in a variety of substantive areas. Yang and Chen [l] described a fuzzy generalized k-NN algorithm which is a unified approach to a variety of fuzzy k-NNR’s. They created the strong consistency of posterior risk of the fuzzy generalized NNR. In this paper, we give their convergence rate. That is, the convergence rate of posterior risk of the...

Learning from imbalanced data is one of the burning issues of the era. Traditional classification methods exhibit degradation in their performances while dealing with imbalanced data sets due to skewed distribution of data into classes. Among various suggested solutions, instance based weighted approaches secured the space in such cases. In this paper, we are proposing a new fuzzy weighted near...

We introduce a new approach for predicting the secondary structure of proteins using profiles and the Fuzzy K-Nearest Neighbor algorithm. K-Nearest Neighbor methods give relatively better performance than Neural Networks or Hidden Markov models when the query protein has few homologs in the sequence database to build sequence profile. Although the traditional K-Nearest Neighbor algorithms are a...

In this paper, classification efficiency of the conventional K-nearest neighbor algorithm is enhanced by exploiting fuzzy-rough uncertainty. The simplicity and nonparametric characteristics of the conventional K-nearest neighbor algorithm remain intact in the proposed algorithm. Unlike the conventional one, the proposed algorithm does not need to know the optimal value of K. Moreover, the gener...