Open AccessPosted Content
Survey of Nearest Neighbor Techniques
Nitin Bhatia,Vandana +1 more
TLDR
The nearest neighbor (NN) technique is very simple, highly efficient and effective in the field of pattern recognition, text categorization, object recognition etc and structure based techniques reduce the computational complexity.Abstract:
The nearest neighbor (NN) technique is very simple, highly efficient and effective in the field of pattern recognition, text categorization, object recognition etc. Its simplicity is its main advantage, but the disadvantages can't be ignored even. The memory requirement and computation complexity also matter. Many techniques are developed to overcome these limitations. NN techniques are broadly classified into structure less and structure based techniques. In this paper, we present the survey of such techniques. Weighted kNN, Model based kNN, Condensed NN, Reduced NN, Generalized NN are structure less techniques whereas k-d tree, ball tree, Principal Axis Tree, Nearest Feature Line, Tunable NN, Orthogonal Search Tree are structure based algorithms developed on the basis of kNN. The structure less method overcome memory limitation and structure based techniques reduce the computational complexity.read more
Citations
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Proceedings Article
Submodularity in Data Subset Selection and Active Learning
TL;DR: The connection of submodularity to the data likelihood functions for Naive Bayes and Nearest Neighbor classifiers is shown, and the data subset selection problems for these classifiers are formulated as constrained submodular maximization.
Proceedings ArticleDOI
Traffic prediction in a bike-sharing system
TL;DR: A hierarchical prediction model to predict the number of bikes that will be rent from/returned to each station cluster in a future period so that reallocation can be executed in advance and its advantage beyond baseline approaches is confirmed.
Proceedings ArticleDOI
Breast cancer classification using machine learning
TL;DR: Two different classifiers are presented: Naive Bayes (NB) classifier and knearest neighbor (KNN) for breast cancer classification and a comparison between the two new implementations is proposed and their accuracy is evaluated using cross validation.
Posted Content
Classification of Heart Disease Using K- Nearest Neighbor and Genetic Algorithm
TL;DR: In this article, a new algorithm which combines KNN with genetic algorithm for effective classification is proposed, which enhances the accuracy in diagnosis of heart disease in Andhra Pradesh, which is the leading cause of mortality accounting for 32%of all deaths, a rate as high as Canada (35%) and USA.
Journal ArticleDOI
Data science for building energy management: a review
Miguel Molina-Solana,Miguel Molina-Solana,María Ros,M. Dolores Ruiz,Juan Gómez-Romero,Maria J. Martin-Bautista +5 more
TL;DR: In this paper, the authors reviewed how Data Science has been applied to address the most difficult problems faced by practitioners in the field of Energy Management, especially in the building sector. And they also discussed the challenges and opportunities that will arise with the advent of fully connected devices and new computational technologies.
References
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Journal ArticleDOI
Nearest neighbor pattern classification
Thomas M. Cover,Peter E. Hart +1 more
TL;DR: The nearest neighbor decision rule assigns to an unclassified sample point the classification of the nearest of a set of previously classified points, so it may be said that half the classification information in an infinite sample set is contained in the nearest neighbor.
Book ChapterDOI
KNN Model-Based Approach in Classification
TL;DR: This paper proposes a novel kNN type method for classification that reduces the dependency on k, makes classification faster, and compares well with C5.0 and kNN in terms of classification accuracy.
Proceedings Article
Using Text Categorization Techniques for Intrusion Detection
Yihua Liao,V. Rao Vemuri +1 more
TL;DR: A new approach, based on the k-Nearest Neighbor (kNN) classifier, is used to classify program behavior as normal or intrusive, and preliminary experiments with 1998 DARPA BSM audit data show that the kNN classifier can effectively detect intrusive attacks and achieve a low false positive rate.
Proceedings ArticleDOI
Query dependent ranking using K-nearest neighbor
TL;DR: This paper proposes a K-Nearest Neighbor (KNN) method for query-dependent ranking, and proves a theory which indicates that the approximations are accurate in terms of difference in loss of prediction, if the learning algorithm used is stable with respect to minor changes in training examples.