Vibration-based quality monitoring of manufactured components often employs pattern recognition methods. Albeit developing several classification methods, they usually provide high accuracy for specific types datasets, but not general cases. In this paper, issue has been addressed by a novel ensemble classifier based on the Dempster-Shafer theory evidence. proposed procedure, prior to DST combi...

the improvement of high-through-put gene profiling based microarrays technology has provided monitoring the expression value of thousands of genes simultaneously. detailed examination of changes in expression levels of genes can help physicians to have effi cient diagnosing, classification of tumors and cancer’s types as well as effective treatments. finding genes that can classify the group of...

We propose two novel Tensor Voting (TV) based supervised binary classification algorithms for N-Dimensional (N-D) data points. (a) The first one finds an approximation to a separating hyper-surface that best separates the given two classes in N-D: this is done by finding a set of candidate decision-surface points (using the training data) and then modeling the decision surface by local planes u...

In this paper, a novel center-based nearest neighbor (CNN) classifier is proposed to deal with the pattern classification problems. Unlike nearest feature line (NFL) method, CNN considers the line passing through a sample point with known label and the center of the sample class. This line is called the center-based line (CL). These lines seem to have more capacity of representation for sample ...

In this work we propose to use fuzzy prototypes for classification tasks. We create the prototypes by first clusterizing, separately, the available data for each class. Then we create a fuzzy set around each class center, according to clusterization indices that take into account both local and global data. We also investigate the use of an index to guide the process of obtaining a suitable num...

Bayesian Classifiers Learn joint distribution P(C,F) Assign to f the most probable class label argmaxc′∈C P(c′, f̃) This defines a classifier, i.e., a map: (F1× . . .×Fm)→ C Credal Classifiers Learn joint credal set P(C,F) Set of optimal classes (e.g., according to maximality ) {c′ ∈ C |@c′′ ∈ C ,∀P ∈ P : P(c′′|f̃) > P(c′|f̃)} This defines a credal classifier, i.e., (F1× . . .×Fm)→ 2 May return mo...

Many methods have been proposed for combining multiple classifiers in pattern recognition such as Random Forest which uses decision trees for problem solving. In this paper, we propose a weighted vote-based classifier ensemble method. The proposed method is similar to Random Forest method in employing many decision trees and neural networks as classifiers. For evaluating the proposed weighting ...

In many applications, it is desirable to build a classifier that is bounded within an interval. Our motivating example is rooted in monitoring in a stamping process. A novel approach is proposed and examined in this paper. Our method consists of three stages: (1) A baseline of each class is estimated via convex optimization; (2) An “optimal interval” that maximizes the difference among the base...