In this paper, we propose a hybrid decision model for supporting the ranking financial status of corporations using case-based reasoning augmented with genetic algorithms and the fuzzy nearest neighbor method. An empirical experimentation on 746 cases was conducted that shows that the average accuracy of the ranking is about 92% and 80% for the first order and the second order, respectively. Th...

The convergence properties of a nearest neighbor rule that uses an editing procedure to reduce the number of preclassified samples and to improve the performance of the rule are developed. Editing of the preclassified samples using the three-nearest neighbor rule followed by classification using the single-nearest neighbor rule with the remaining preclassified samples appears to produce a decis...

A reliable gait features are required to extract the gait sequences from an images. In this paper suggested a simple method for gait identification which is based on moments. Moment values are extracted on different number of frames of Gray Scale and Silhouette images of CASIA database. These moment values are considered as feature values. Fuzzy logic and Nearest Neighbor Classifier are used fo...

We present in this paper a simple, yet valuable improvement to the traditional k-Nearest Neighbor (kNN) classifier. It aims at addressing the issue of unbalanced classes by maximizing the class-wise classification accuracy. The proposed classifier also gives the option of favoring a particular class through evaluating a small set of fuzzy rules. When tested on a number of UCI datasets, the prop...

We describe the first algorithm and software, RNAenn, to compute the partition function and minimum free energy secondary structure for RNA with respect to an extended nearest neighbor energy model. Our next-nearest-neighbor triplet energy model appears to lead to somewhat more cooperative folding than does the nearest neighbor energy model, as judged by melting curves computed with RNAenn and ...

k-Nearest Neighbor (KNN) is one of the most popular algorithms for pattern recognition. Many researchers have found that the KNN classifier may decrease the precision of classification because of the uneven density of t raining samples .In view of the defect, an improved k-nearest neighbor algorithm is presented using shared nearest neighbor similarity which can compute similarity between test ...