A k nearest neighbor (kNN) classifier classifies a query instance to the most frequent class of its k nearest neighbors in the training instance space. For imbalanced class distribution, a query instance is often overwhelmed by majority class instances in its neighborhood and likely to be classified to the majority class. We propose to identify exemplar minority class training instances and gen...

Several classifiers for forward looking infra-red imagery are designed and implemented, and their relative performance is benchmarked on 2545 images belonging to 8 different ship classes, from which 11 attributes are extracted. These are a Bayes classifier, a Dempster–Shafer classifier ensemble in which specialized classifiers are optimized to return a single ship class, a k-nearest neighbor cl...

The rapid development of the internet things (IoT) has taken an important role in daily activities. As it develops, IoT is very vulnerable to attacks and creates for users. Intrusion detection system (IDS) can work efficiently look activity network. Many data sets have already been collected, however, when dealing with problems involving big hight imbalances. This article proposes, using datase...

In traditional text classification, a classifier is built using labeled training documents of every class. This paper studies a different problem. Given a set P of documents of a particular class (called positive class) and a set U of unlabeled documents that contains documents from class P and also other types of documents (called negative class documents), we want to build a classifier to cla...

Soil Salinity has been a large problem in arid and semi arid regions. Preparation of such maps is useful for Natural resource managers. Old methods of preparing such maps require a lot of time and cost. Multi-spectral remotely sensed dates due to the broad vision and repeating of these imageries is suitable for provide saline soil maps. This investigation is conducted to provide saline soil map...

Many state-of-the-art algorithms for object class recognition have recently appeared in the literature. These algorithms recognize one object at a time in that a dedicated classifier needs to be trained for each object class. However, no paper has yet reported a single classifier capable of recognizing the object class of any one of a number of classes. This paper sets out to recognize objects ...

In this paper, we propose a Dynamic Naive Bayesian (DNB) network model for classifying data sets with hierarchical labels. The DNB model is built upon a Naive Bayesian (NB) network, a successful classifier for data with flattened (nonhierarchical) class labels. The problems using flattened class labels for hierarchical classification are addressed in this paper. The DNB has a top-down structure...

Problems in which abnormal or novel situations should be detected can be approached by describing the domain of the class of typical examples. These applications come from the areas of machine diagnostics, fault detection, illness identification, or, in principle, refer to any problem where little knowledge is available outside the typical class. In this paper, we explain why proximities are na...