Ensemble learning approach in improved K Nearest Neighbor algorithm for Text categorization

Iswarya, P.; Radha, V.

doi:10.1109/iciiecs.2015.7193250

Cited by 5 publications

(2 citation statements)

References 18 publications

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“…It identifies the k nearest neighbors of a test document in the training set and assesses class candidates according to their classes. Iswarya and Radha [16] suggested an Ensemble learning strategy for the Improved KNN method for text categorization (EINNTC), which use one-pass clustering to reduce similarity calculation time and minimize noisy samples. In the first stage, a classification model is developed and updated, and in the second step, ensemble learning is used to determine the ideal value for the parameter K. In terms of F1 score, the results demonstrate that EINNTC surpasses SVM and conventional KNN.…”

Section: K-nearst Neibghors (Knn)mentioning

confidence: 99%

Text classification supervised algorithms with term frequency inverse document frequency and global vectors for word representation: a comparative study

Labd,

Bahassine,

Housni

et al. 2024

IJECE

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Over the course of the previous two decades, there has been a rise in the quantity of text documents stored digitally. The ability to organize and categorize those documents in an automated mechanism, is known as text categorization which is used to classify them into a set of predefined categories so they may be preserved and sorted more efficiently. Identifying appropriate structures, architectures, and methods for text classification presents a challenge for researchers. This is due to the significant impact this concept has on content management, contextual search, opinion mining, product review analysis, spam filtering, and text sentiment mining. This study analyzes the generic categorization strategy and examines supervised machine learning approaches and their ability to comprehend complex models and nonlinear data interactions. Among these methods are k-nearest neighbors (KNN), support vector machine (SVM), and ensemble learning algorithms employing various evaluation techniques. Thereafter, an evaluation is conducted on the constraints of every technique and how they can be applied to real-life situations.

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Section: K-nearst Neibghors (Knn)mentioning

confidence: 99%

Text classification supervised algorithms with term frequency inverse document frequency and global vectors for word representation: a comparative study

Labd,

Bahassine,

Housni

et al. 2024

IJECE

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“…If the user requires an absolute response, then the document category that represents the most neighbours is returned. [9][10] However, if a user wanted a fuzzy request, the percentage for each category is returned.…”

Section: Implementing a Text Classifiermentioning

confidence: 99%

Using bipartite graphs projected onto two dimensions for text classification

Rozaki¹,

Redmond²

2017

Fifth International Conference on Advances in Computing, Communication and Information Technology - CCIT 2017

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Abstract-In our Big Data world, the amount of text being gathered is ever expanding. For many years, data curators have sought ways to group these documents and identify common topics. As the size of the problem increases, solutions that will scale are needed. The purpose of this work is to present a novel text classifier that can be used for text-mining and interactive information access. The model that is demonstrated can be used to extract hierarchical relations between topics, as well as to conducted unsupervised clustering of documents and keywords. The approach that is taken with this model is the use of a graphof-words key term extraction and a dimensional projection of the bipartite graph of documents and key terms. This projection makes it possible for terms to be co-clustered in an efficient manner in relation to their documents and the documents in relation to their terms. Furthermore, the key term extraction process that is outlined can be scaled on a large corpus using a distributed processing system such as Apache Spark, and the resultant model can be visually interacted with by users.

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Performance of distance-based k-nearest neighbor classification method using local mean vector and harmonic distance

Sinaga

Tulus

Sihombing

2020

IOP Conf. Ser.: Mater. Sci. Eng.

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K-Nearest Neighbor was one of the top ten algorithms data mining in the classification process. The low accuracy results in the K-Nearest Neighbor classification method was caused of this method used the system of majority vote which allowed the selection of outliers as the closest neighbors and in the distance model used as a method of determining similarity between data. In this process it is evident that local mean vector and harmonic distance can improve accuracy, where the highest increase in average accuracy obtained in the set data wine is equal to 6.29% and the highest accuracy increase for LMKNN is obtained in set data glass identification which is 16.18%. Based on the tests that had been conducted on all data sets used, it could be seen that the proposed method was able to provide a better value of accuracy than the value of accuracy produced by traditional K-Nearest Neighbor and LMKNN.

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Ensemble learning approach in improved K Nearest Neighbor algorithm for Text categorization

Cited by 5 publications

References 18 publications

Text classification supervised algorithms with term frequency inverse document frequency and global vectors for word representation: a comparative study

Text classification supervised algorithms with term frequency inverse document frequency and global vectors for word representation: a comparative study

Using bipartite graphs projected onto two dimensions for text classification

Performance of distance-based k-nearest neighbor classification method using local mean vector and harmonic distance

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