In this paper, we consider K-class classification problem, a significant issue in machine learning or artificial intelligence. In this problem, we are given a training set of samples, where each sample is represented by a nominal-valued vector and is labeled as one of the predefined K classes. The problem asks to construct a classifier that predicts the classes of future samples with high accur...

Supervised classifiers are commonly employed in remote sensing to extract land cover information, but various factors affect their accuracy. The number of available training samples, in particular, is known to have a significant impact on classification accuracies. Obtaining a sufficient number of samples is, however, not always practical. The support vector machine (SVM) is a supervised classi...

Naive Bayes is among the simplest probabilistic classifiers. It often performs surprisingly well in many real world applications, despite the strong assumption that all features are conditionally independent given the class. In the learning process of this classifier with the known structure, class probabilities and conditional probabilities are calculated using training data, and then values o...

handwritten digit recognition can be categorized as a classification problem. probabilistic neural network (pnn) is one of the most effective and useful classifiers, which works based on bayesian rule. in this paper, in order to recognize persian (farsi) handwritten digit recognition, a combination of intelligent clustering method and pnn has been utilized. hoda database, which includes 80000 p...

Multiple classifiers combination is a technique that combines the decisions of different classifiers as to reduce the variance of estimation errors and improve the overall classification accuracy. A new multiple classifiers fusion method integrated classifier selection and classifier combination is proposed in this paper. It is base on interval-valued fuzzy permutation. Firstly, normalize all c...

An investigation is carried out to formulate some theoretical results regarding the behavior of a genetic-algorithm-based pattern classification methodology, for an infinitely large number of training data points n, in an N-dimensional space RN. It is proved that for nPR, and for a sufficiently large number of iterations, the performance of this classifier (when hyperplanes are considered to ge...

MOTIVATION Measurements are commonly taken from two phenotypes to build a classifier, where the number of data points from each class is predetermined, not random. In this 'separate sampling' scenario, the data cannot be used to estimate the class prior probabilities. Moreover, predetermined class sizes can severely degrade classifier performance, even for large samples. RESULTS We employ sim...

Regularization involves a large family of the state-of-the-art techniques in classifier learning. However, since traditional regularization methods essentially derive from ill-posed multivariate functional fitting problems which can be viewed as a kind of regression, in classifier design, they usually give more concerns to the smoothness of the classifier, and do not sufficiently use the prior ...

Protein subcellular localization prediction plays an important role for understanding the functions and biological processes that proteins are involved in. By using protein sequence information, we can predict where a protein belongs to. In this paper, we propose a new linear classifier for predicting subcellular localizations of proteins using improved features extracted from protein sequences...

ROC analysis investigates and employs the relationship between sensitivity and specificity of a binary classifier. Sensitivity or true positive rate measures the proportion of positives correctly classified; specificity or true negative rate measures the proportion of negatives correctly classified. Conventionally, the true positive rate tpr is plotted against the false positive rate fpr, which...