By cluster analysis, all dipeptides are classified into 16 categories according to their hydrophobicity, Based on the composition of dipeptide categories, a novel representation of protein sequences is proposed here to predict the subcellular location of apoptosis protein sequences. Using K-Nearest Neighbor Classifier, and test on a known dataset which includes 317 apoptosis proteins , the high...

Constrained k nearest neighbor query for uncertain object in the network is to find k uncertain objects which are the k nearest neighbors with range constraint of the query object in the network. For solving this problem, the uncertain object is modeled as the fuzzy object and the network  -distance between fuzzy objects in the network is defined. Base on them, the concept of constrained k nea...

The classical k nearest neighbor (k-nn) classification assumes that a fixed global metric is defined and searching for nearest neighbors is always based on this global metric. In the paper we present a model with local induction of a metric. Any test object induces a local metric from the neighborhood of this object and selects k nearest neighbors according to this locally induced metric. To in...

The current discriminant analysis method design is generally independent of classifiers, thus the connection between discriminant analysis methods and classifiers is loose. This paper provides a way to design discriminant analysis methods that are bound with classifiers. We begin with a local mean based nearest neighbor (LM-NN) classifier and use its decision rule to supervise the design of a d...

In this work we introduce a new distance estimation technique by boosting and we apply it to the K-Nearest Neighbor Classifier (KNN). Instead of applying AdaBoost to a typical classification problem, we use it for learning a distance function and the resulting distance is used into K-NN. The proposed method (Boosted Distance with Nearest Neighbor) outperforms the AdaBoost classifier when the tr...

In this paper we propose a novel classification method for the multiple k-nearest neighbor (MkNN) classifier and show its practical application to medical image processing. The proposed method performs fine classification when a pair of the spatial coordinate of the observation data in the observation space and its corresponding feature vector in the feature space is provided. The proposed MkNN...