Optimal Multi-class Classification with Principal Components
نویسنده
چکیده
An approach to build a multi-class classifier is proposed in this paper. This approach consists of a derivation to show under which loss function an optimal classifier can be obtained. It also consists of a method of selection of principal components for multi-class classification through univariate logistic regressions. And it consists of a derivation of certain derivatives to rank the features using two-layered neural network classifier. An experiment of using a two-layered neural network to test the proposed approach was carried out. The performance of the proposed method of dada reduction was found better than those of some other methods in this particular experiment. And the features that have high ranking of influence were found credible.
منابع مشابه
Negative Selection Based Data Classification with Flexible Boundaries
One of the most important artificial immune algorithms is negative selection algorithm, which is an anomaly detection and pattern recognition technique; however, recent research has shown the successful application of this algorithm in data classification. Most of the negative selection methods consider deterministic boundaries to distinguish between self and non-self-spaces. In this paper, two...
متن کاملDetection of Fake Accounts in Social Networks Based on One Class Classification
Detection of fake accounts on social networks is a challenging process. The previous methods in identification of fake accounts have not considered the strength of the users’ communications, hence reducing their efficiency. In this work, we are going to present a detection method based on the users’ similarities considering the network communications of the users. In the first step, similarity ...
متن کاملApproaches for Multi-Class Discriminant Analysis for Ranking Principal Components
The problem of ranking features computed by principal component analysis (PCA) in N-class problems have been addressed by the multi-class discriminant principal component analysis (MDPCA) and the Fisher discriminability criterion (FDC). These methods are motivated by the fact that PCA components do not necessarily represent important discriminant directions to separate sample groups. Given a da...
متن کاملIdentification of Houseplants Using Neuro-vision Based Multi-stage Classification System
In this paper, we present a machine vision system that was developed on the basis of neural networks to identify twelve houseplants. Image processing system was used to extract 41 features of color, texture and shape from the images taken from front and back of the leaves. The features were fed into the neural network system as the recognition criteria and inputs. Multilayer perceptron (MLP) ne...
متن کاملExploiting Associations between Class Labels in Multi-label Classification
Multi-label classification has many applications in the text categorization, biology and medical diagnosis, in which multiple class labels can be assigned to each training instance simultaneously. As it is often the case that there are relationships between the labels, extracting the existing relationships between the labels and taking advantage of them during the training or prediction phases ...
متن کامل