WITHDRAWN: Text classification using Naïve Bayes classifier

Kolluri, Johnson; Razia, Shaik

doi:10.1016/j.matpr.2020.10.058

Cited by 25 publications

(7 citation statements)

References 16 publications

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“…Naive Bayes (NB) is a Bayes' theory-based classification approach and the condition of predictor independence, where a feature's presence in a class is unrelated to the incidence of any other characteristic [32]. The NB classifier is simple to build and highly successful when dealing large amounts of data.…”

Section: Modelsmentioning

confidence: 99%

Breast invasive ductal carcinoma diagnosis using machine learning models and Gabor filter method of histology images

Kadhim

Kamil²

2023

IJRES

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Breast cancer is the most common type of cancer in women and the leading cause of death from a malignant growth in the world. Machine learning methods have been created to help with cancer detection accuracy. There are several methods for detecting cancer. Histopathological images are more accurate. In this study, we employed the Gabor filter to extract statistical features from invasive ductal carcinoma histopathology images. From the histopathological images, we chose 100, 200, 400, 1000, and 2000 at random. These statistical features were used to train several models to classify these images as malignant or benign, including the decision tree, quadratic discriminant analysis, extra randomized trees, gradient boosting, Gaussian process, Naive Bayes, nearest centroid, multilayer perceptron, and support vector machine. The models' accuracy, sensitivity, specificity, precision, and F1_score were examined. The models produced the highest results when there were 100 images and a wavenumber of 0.2. While as the number of images increased, the models' effectiveness reduced. The most obvious finding to emerge from this study is that we suggest using deep learning instead of machine learning models for large datasets.

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Section: Modelsmentioning

confidence: 99%

Breast invasive ductal carcinoma diagnosis using machine learning models and Gabor filter method of histology images

Kadhim

Kamil²

2023

IJRES

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“…In the literature review conducted by Pintas et al, 13 SVM and NB emerged as the predominant classifiers commonly employed for evaluating feature selection (FS) methods. In our study, we followed this established practice, assessing the results using these two conventional classifiers: SVM 1 with a linear kernel and the multinomial variant of NB 58 …”

Section: Experimentation Workmentioning

confidence: 99%

Feature redundancy removal for text classification using correlated feature subsets

Farek,

Benaidja

2023

Computational Intelligence

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The curse of high dimensionality in text classification is a worrisome problem that requires efficient and optimal feature selection (FS) methods to improve classification accuracy and reduce learning time. Existing filter‐based FS methods evaluate features independently of other related ones, which can then lead to selecting a large number of redundant features, especially in high‐dimensional datasets, resulting in more learning time and less classification performance, whereas information theory‐based methods aim to maximize feature dependency with the class variable and minimize its redundancy for all selected features, which gradually becomes impractical when increasing the feature space. To overcome the time complexity issue of information theory‐based methods while taking into account the redundancy issue, in this article, we propose a new feature selection method for text classification termed correlation‐based redundancy removal, which aims to minimize the redundancy using subsets of features having close mutual information scores without sequentially seeking already selected features. The idea is that it is not important to assess the redundancy of a dominant feature having high classification information with another irrelevant feature having low classification information and vice‐versa since they are implicitly weakly correlated. Our method, tested on seven datasets using both traditional classifiers (Naive Bayes and support vector machines) and deep learning models (long short‐term memory and convolutional neural networks), demonstrated strong performance by reducing redundancy and improving classification compared to ten competitive metrics.

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“…where n is the number of variables, y is the class variable, x i until x n is the dependent feature vector [9]. Linear Support Vector Classification (Linear SVC) and Support Vector Classification (SVC) are two major forms of SVC that can do multiclass classification.…”

Section: Topic Classification Model Developmentmentioning

confidence: 99%

Autonomous Language Processing and Text Mining by Data Analytics for Business Solutions

Wong¹,

Tan²,

Kor³

et al. 2022

Proceedings of the International Conference on Mathematical Sciences and Statistics 2022 (ICMSS 2022)

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Speech analytics solution is a technology that enables a company to discover customer's patterns and insights by analyzing relevant data, such as recorded audio files or phone conversations. The accuracy of speech recognition or speechto-text transcription has been a challenge all along. This paper aims to present a text classification model for the call transcriptions based on the context, and to improve the accuracy of Google Speech API in Malay language. In this study, the accuracy of speech-to-text transcription is measured by word recognition rate and an accuracy scale. Time-cut-point and audio speed are the factors investigated to determine whether these factors affect the accuracy of text transcription. The results obtained from different time-cut-point and audio speed setting have been studied to identify the best combination. Furthermore, the pre-processed text data is utilized to train the text classification model using Support Vector Machine and Naive Bayes algorithms. In this paper, two approaches have been studied to improve Google Speech API. The first approach is to apply speech adaptation, which is the function made by Google. However, it showed that the accuracy dropped when 250 words were added into the speech adaptation, or when the audio speed was lowered. This is because the words error rate for both methods have increased. In the second approach, removing speech adaptation and lowering audio speed simultaneously caused a decrease in words error rate, hence the accuracy increased. In a nutshell, Support Vector Machine has better accuracy score of text classification as compared with Naive Bayes algorithms. As a result, short time-cut-point with normal speed of audio file showed a positive impact to improve Google speech-to-text API, along with Support Vector Machine being more suitable for classification model.

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WITHDRAWN: Text classification using Naïve Bayes classifier

Cited by 25 publications

References 16 publications

Breast invasive ductal carcinoma diagnosis using machine learning models and Gabor filter method of histology images

Breast invasive ductal carcinoma diagnosis using machine learning models and Gabor filter method of histology images

Feature redundancy removal for text classification using correlated feature subsets

Autonomous Language Processing and Text Mining by Data Analytics for Business Solutions

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