Design and Evaluation of Outlier Detection Based on Semantic Condensed Nearest Neighbor

Abstract

<jats:title>Abstract</jats:title> <jats:p>Social media contain abundant information about the events or news occurring all over the world. Social media growth has a greater impact on various domains like marketing, e-commerce, health care, e-governance, and politics, etc. Currently, Twitter was developed as one of the social media platforms, and now, it is one of the most popular social media platforms. There are 1 billion user’s profiles and millions of active users, who post tweets daily. In this research, buzz detection in social media was carried out by the semantic approach using the condensed nearest neighbor (SACNN). The Twitter and Tom’s Hardware data are stored in the UC Irvine Machine Learning Repository, and this dataset is used in this research for outlier detection. The min–max normalization technique is applied to the social media dataset, and additionally, missing values were replaced by the normalized value. The condensed nearest neighbor (CNN) is used for semantic analysis of the database, and based on the optimized value provided by the proposed method, the threshold is calculated. The threshold value is used to classify buzz and non-buzz discussions in the social media database. The result showed that the SACNN achieved 99% of accuracy, and relative error is less than the existing methods.</jats:p>

Layer-Wise Relevance Propagation Based Sample Condensation for Kernel Machines

“…This observation has been made before, e.g. when working with nearest neighbor based classifiers [2,12]. Thus, there is a general need of sample condensation for kernel machines.…”

“…Sample condensation has been studied in other contexts, where the whole training set has to be saved and used for classification. Starting from the pioneer work [6], more advanced techniques have been proposed to boost the performance of nearest neighbor based classifiers [2,12]. In addition, nearest neighbor condensation has been applied to speed up the training of support vector machines [1] and convolutional neural networks [12].…”