ML-kNN is a well-known algorithm for multi-label classification. Although effective in some cases, ML-kNN has some defect due to the fact that it is a binary relevance classifier which only considers one label every time. In this paper, we present a new method for multi-label classification, which is based on lazy learning approaches to classify an unseen instance on the basis of its k nearest ...

In this paper, we introduce Sequential Classifiers Combination (SCC) into text categorization to improve both the classification effectiveness and classification efficiency of the combined individual classifiers. We apply two classifiers sequentially for experimental study, where the first classifier (called filtering classifier) is used to generate candidate categories for the test document an...

Classification of spatial data streams is crucial, since the training dataset changes often. Building a new classifier each time can be very costly with most techniques. In this situation, k-nearest neighbor (KNN) classification is a very good choice, since no residual classifier needs to be built ahead of time. KNN is extremely simple to implement and lends itself to a wide variety of variatio...

A flood is an overflow of water and becomes the common natural disaster. Prediction of a flood is one of the challenges for disaster management around the world especially in developing countries. Thus, more accurate flood prediction models have been investigated according to the geographical locations. In this paper, we have studied and compared some useful machine learning models such as KNN,...

In this paper, we address the problem of finding k-nearest neighbors (KNN) in sequence databases using the edit distance. Unlike most existing works using short and exact ngram matchings together with a filter-and-refine framework for KNN sequence search, our new approach allows us to use longer but approximate n-gram matchings as a basis of KNN candidates pruning. Based on this new idea, we de...

The Internet provides easy access to a kind of library resources. However, classification of documents from a large amount of data is still an issue and demands time and energy to find certain documents. Classification of similar documents in specific classes of data can reduce the time for searching the required data, particularly text documents. This is further facilitated by using Artificial...

Dr.M. CHIDAMBARAM Msc, Mphil, MBA, PhD (Asst.Proff) Rajah Serfoji Government Arts College,Tanjore ---------------------------------------------------------------------***--------------------------------------------------------------------ABSTRACT:A moving kNN query continuously reports the k results (restaurants) nearest to a moving query point (tourist). In addition to the query results, a ser...

This paper proposes a kNN model-based feature selection method aimed at improving the efficiency and effectiveness of the ReliefF method by: (1) using a kNN model as the starter selection, aimed at choosing a set of more meaningful representatives to replace the original data for feature selection; (2) integration of the Heterogeneous Value Difference Metric to handle heterogeneous applications...

DOI reference number: 10.18293/SEKE2015-153 Abstract—Recommendation systems are software tools and techniques that provide customized content to users. The collaborative filtering is one of the most prominent approaches in the recommendation area. Among the collaborative algorithms, one of the most popular is the k-Nearest Neighbors (kNN) which is an instance-based learning method. The kNN gene...

Oxide interface engineering has attracted considerable attention since the discovery of its exotic properties induced by lattice strain, dislocation and composition change at the interface. In this paper, the atomic resolution structure and composition of the interface between the lead-free piezoelectric (K0.5Na0.5)NbO3 (KNN) thin films and single-crystalline SrTiO3 substrate were investigated ...