Text Categorization with K-Nearest Neighbor Approach

Manne, Suneetha; Kotha, Sita Kumari; Fatima, S. Sameen

doi:10.1007/978-3-642-27443-5_47

Cited by 10 publications

(6 citation statements)

References 5 publications

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“…3 Consequently, KNN has been studied over the past few decades and widely applied in many fields. 4 Thus, KNN comprises the baseline classifier in many pattern classification problems such as pattern recognition, 5 text categorization, 6 ranking models, 7 object recognition, 8 and event recognition 9 applications. KNN is a nonparamet-ric algorithm.…”

Section: Introductionmentioning

confidence: 99%

Effects of Distance Measure Choice on K-Nearest Neighbor Classifier Performance: A Review

et al. 2019

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The K-nearest neighbor (KNN) classifier is one of the simplest and most common classifiers, yet its performance competes with the most complex classifiers in the literature. The core of this classifier depends mainly on measuring the distance or similarity between the tested examples and the training examples. This raises a major question about which distance measures to be used for the KNN classifier among a large number of distance and similarity measures available? This review attempts to answer this question through evaluating the performance (measured by accuracy, precision, and recall) of the KNN using a large number of distance measures, tested on a number of real-world data sets, with and without adding different levels of noise. The experimental results show that the performance of KNN classifier depends significantly on the distance used, and the results showed large gaps between the performances of different distances. We found that a recently proposed nonconvex distance performed the best when applied on most data sets comparing with the other tested distances. In addition, the performance of the KNN with this top performing distance degraded only *20% while the noise level reaches 90%, this is true for most of the distances used as well. This means that the KNN classifier using any of the top 10 distances tolerates noise to a certain degree. Moreover, the results show that some distances are less affected by the added noise comparing with other distances.

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Section: Introductionmentioning

confidence: 99%

Effects of Distance Measure Choice on K-Nearest Neighbor Classifier Performance: A Review

et al. 2019

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“…Stop Words have been In order to compare efficiency of LS and DVS for feature selection, features selected by LS and DVS are tested by classifiers. The commonly used classifiers for text classification include Naïve Bayes [39,40], -nearest neighbor [40,41], neural network [42,43], support vector machine (SVM, [40]), and decision tree (DT, [40,43]). This paper chooses DT(C5.0) and SVM to test features selected by LS and DVS because DT has been used either as main classification tool or as baseline classifier and SVM offers two advantages for text classification, according to Fabrizio's paper [43].…”

Section: Methodsmentioning

confidence: 99%

Distance Variance Score: An Efficient Feature Selection Method in Text Classification

Wang

Hong

2015

Mathematical Problems in Engineering

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With the rapid development of web applications such as social network, a large amount of electric text data is accumulated and available on the Internet, which causes increasing interests in text mining. Text classification is one of the most important subfields of text mining. In fact, text documents are often represented as a high-dimensional sparse document term matrix (DTM) before classification. Feature selection is essential and vital for text classification due to high dimensionality and sparsity of DTM. An efficient feature selection method is capable of both reducing dimensions of DTM and selecting discriminative features for text classification. Laplacian Score (LS) is one of the unsupervised feature selection methods and it has been successfully used in areas such as face recognition. However, LS is unable to select discriminative features for text classification and to effectively reduce the sparsity of DTM. To improve it, this paper proposes an unsupervised feature selection method named Distance Variance Score (DVS). DVS uses feature distance contribution (a ratio) to rank the importance of features for text documents so as to select discriminative features. Experimental results indicate that DVS is able to select discriminative features and reduce the sparsity of DTM. Thus, it is much more efficient than LS.

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“…As a result, KNN has undergone extensive research and widespread application across various fields [74]. Consequently, KNN serves as the foundational classifier in numerous pattern classification problems, including pattern recognition [75], text categorization [76], ranking models [77], object recognition [78], and event recognition [79] applications.…”

Section: Random Forestmentioning

confidence: 99%

Sentiment Classification of Community towards COVID-19 Issues on Twitter (Case Study: Indonesia, March-May 2020)

Daulay,

Rifqi Ramadhan,

Lya Hulliyyatus Suadaa

2023

icdsos

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This study examines sentiment analysis related to COVID-19 in Indonesia (March-May 2020) using InSet Lexicon as training data in supervised machine learning models. The dataset comprises 7,967 tweets, divided into 90% training data and 10% testing data. The results reveal that Support Vector Machine (SVM) and Random Forest (RF) are the most effective methods, achieving accuracy above 80%, with SVM reaching 87% and RF at 86%. InSet Lexicon itself attains an accuracy of 75%, a macro average of 69%, and a weighted average of 74%, making it an effective alternative for large-scale data labeling. Research recommendations support further development of InSet Lexicon for sentiment classification and expansion of the lexicon for foreign languages to enhance sentiment analysis accuracy in a global context. This study provides valuable insights into understanding public sentiment regarding crucial issues such as COVID-19 in Indonesia.

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Text Categorization with K-Nearest Neighbor Approach

Cited by 10 publications

References 5 publications

Effects of Distance Measure Choice on K-Nearest Neighbor Classifier Performance: A Review

Effects of Distance Measure Choice on K-Nearest Neighbor Classifier Performance: A Review

Distance Variance Score: An Efficient Feature Selection Method in Text Classification

Sentiment Classification of Community towards COVID-19 Issues on Twitter (Case Study: Indonesia, March-May 2020)

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