scholarly journals An efficient technique for hybrid classification and feature extraction using normalization

2018 ◽  
Vol 7 (2.27) ◽  
pp. 156 ◽  
Author(s):  
Bipanjyot Kaur ◽  
Gourav Bathla
Keyword(s):  
Feature Extraction ◽  
Feature Selection ◽  
Language Processing ◽  
Text Classification ◽  
Complete Classification ◽  
Learning Technique ◽  
Hybrid Classification ◽  
Classification Evaluation ◽  
Class Labels ◽  
Evaluation Parameters

Text classification is technique for assigning the class or label to a particular document within predefined class labels. Predefined classes examples are sports, business, technical, education and science etc. Classification is supervised learning technique i.e. these classes are trained with certain features and then document is classified based on similarity measure with these trained document set. Text classification is used in many applications like assigning the label to the documents, separating the spam messages from the genuine one, filtering of text, natural language processing etc. Feature selection, extraction and classification are various phases for assigning label to any document. In this paper, PCA is used for feature extraction, ABC is used for feature selection and SVM is used for classification. PCA is improved by applying normalization-using size of features in our proposed approach. It reduces the redundant features to larger extent. There are very few research works, which have implemented PCA, ABC and SVM for complete classification. Evaluation parameters like accuracy, F-measure and G-mean are calculated to check classifier efficiency. The proposed system is deployed on 20-Newsgroup dataset. Experiment analysis proves that accuracy is improved using our proposed approach as compared to existing approaches.  

scholarly journals Spam text classification using LSTM Recurrent Neural Network

2021 ◽  
Vol 9 (9) ◽  
pp. 1271-1275
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Language Processing ◽  
Text Classification ◽  
Short Term Memory ◽  
Experimental Studies ◽  
The Other ◽  
Support Vector ◽  
Data Points ◽  
Class Labels

Sequence Classification is one of the on-demand research projects in the field of Natural Language Processing (NLP). Classifying a set of images or text into an appropriate category or class is a complex task that a lot of Machine Learning (ML) models fail to accomplish accurately and end up under-fitting the given dataset. Some of the ML algorithms used in text classification are KNN, Naïve Bayes, Support Vector Machines, Convolutional Neural Networks (CNNs), Recursive CNNs, Recurrent Neural Networks (RNNs), Long Short Term Memory (LSTM), etc. For this experimental study, LSTM and a few other algorithms were chosen for a more comparative study. The dataset used is the SMS Spam Collection Dataset from Kaggle and 150 more entries were additionally added from different sources. Two possible class labels for the data points are spam and ham. Each entry consists of the class label, a few sentences of text followed by a few useless features that are eliminated. After converting the text to the required format, the models are run and then evaluated using various metrics. In experimental studies, the LSTM gives much better classification accuracy than the other machine learning models. F1-Scores in the high nineties were achieved using LSTM for classifying the text. The other models showed very low F1-Scores and Cosine Similarities indicating that they had underperformed on the dataset. Another interesting observation is that the LSTM had reduced the number of false positives and false negatives than any other model.

scholarly journals English Text Classification Using Improved Recursive Feature Elimination (IRFE) Algorithm: تصنيف النص الإنجليزي باستخدام الخوارزمية العودية المحسنة لإزالة الخواص (IRFE)

2020 ◽  
Vol 4 (2) ◽  
Author(s):  
Esraa H. Abd Al-Ameer, Ahmed H. Aliwy
Keyword(s):  
Feature Selection ◽  
Language Processing ◽  
Text Classification ◽  
Feature Selection Method ◽  
Selection Method ◽  
English Text ◽  
Recursive Feature Elimination ◽  
Chi Square ◽  
Data Set ◽  
New Feature

Documents classification is from most important fields for Natural language processing and text mining. There are many algorithms can be used for this task. In this paper, focuses on improving Text Classification by feature selection. This means determine some of the original features without affecting the accuracy of the work, where our work is a new feature selection method was suggested which can be a general formulation and mathematical model of Recursive Feature Elimination (RFE). The used method was compared with other two well-known feature selection methods: Chi-square and threshold. The results proved that the new method is comparable with the other methods, The best results were 83% when 60% of features used, 82% when 40% of features used, and 82% when 20% of features used. The tests were done with the Naïve Bayes (NB) and decision tree (DT) classification algorithms , where the used dataset is a well-known English data set “20 newsgroups text” consists of approximately 18846 files. The results showed that our suggested feature selection method is comparable with standard Like Chi-square.

Enhancing Effectiveness of Dimension Reduction in Text Classification

2016 ◽  
Vol 26 (03) ◽  
pp. 1750008 ◽  
Author(s):  
Seyyed Hossein Seyyedi ◽  
Behrouz Minaei-Bidgoli
Keyword(s):  
Feature Extraction ◽  
Feature Selection ◽  
Dimension Reduction ◽  
Text Classification ◽  
Field Experiments ◽  
Information Gain ◽  
Dimensional Space ◽  
Feature Space ◽  
Spam Detection ◽  
Selection Methods

Nowadays, text is one prevalent forms of data and text classification is a widely used data mining task, which has various application fields. One mass-produced instance of text is email. As a communication medium, despite having a lot of advantages, email suffers from a serious problem. The number of spam emails has steadily increased in the recent years, leading to considerable irritation. Therefore, spam detection has emerged as a separate field of text classification. A primary challenge of text classification, which is more severe in spam detection and impedes the process, is high-dimensionality of feature space. Various dimension reduction methods have been proposed that produce a lower dimensional space compared to the original. These methods are divided mainly into two groups: feature selection and feature extraction. This research deals with dimension reduction in the text classification task and especially performs experiments in the spam detection field. We employ Information Gain (IG) and Chi-square Statistic (CHI) as well-known feature selection methods. Also, we propose a new feature extraction method called Sprinkled Semantic Feature Space (SSFS). Furthermore, this paper presents a new hybrid method called IG_SSFS. In IG_SSFS, we combine the selection and extraction processes to reap the benefits from both. To evaluate the mentioned methods in the spam detection field, experiments are conducted on some well-known email datasets. According to the results, SSFS demonstrated superior effectiveness over the basic selection methods in terms of improving classifiers’ performance, and IG_SSFS further enhanced the performance despite consuming less processing time.

scholarly journals An Automated ECG Beat Classification System Using Deep Neural Networks with an Unsupervised Feature Extraction Technique

2019 ◽  
Vol 9 (14) ◽  
pp. 2921 ◽  
Author(s):  
Siti Nurmaini ◽  
Radiyati Umi Partan ◽  
Wahyu Caesarendra ◽  
Tresna Dewi ◽  
Muhammad Naufal Rahmatullah ◽  
...  
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Feature Extraction ◽  
Feature Selection ◽  
Deep Neural Networks ◽  
Machine Learning Technique ◽  
Proposed Model ◽  
Learning Technique ◽  
Processing Steps ◽  
Unsupervised Feature Extraction

An automated classification system based on a Deep Learning (DL) technique for Cardiac Disease (CD) monitoring and detection is proposed in this paper. The proposed DL architecture is divided into Deep Auto-Encoders (DAEs) as an unsupervised form of feature learning and Deep Neural Networks (DNNs) as a classifier. The objective of this study is to improve on the previous machine learning technique that consists of several data processing steps such as feature extraction and feature selection or feature reduction. It is also noticed that the previously used machine learning technique required human interference and expertise in determining robust features, yet was time-consuming in the labeling and data processing steps. In contrast, DL enables an embedded feature extraction and feature selection in DAEs pre-training and DNNs fine-tuning process directly from raw data. Hence, DAEs is able to extract high-level of features not only from the training data but also from unseen data. The proposed model uses 10 classes of imbalanced data from ECG signals. Since it is related to the cardiac region, abnormality is usually considered for an early diagnosis of CD. In order to validate the result, the proposed model is compared with the shallow models and DL approaches. Results found that the proposed method achieved a promising performance with 99.73% accuracy, 91.20% sensitivity, 93.60% precision, 99.80% specificity, and a 91.80% F1-Score. Moreover, both the Receiver Operating Characteristic (ROC) curve and the Precision-Recall (PR) curve from the confusion matrix showed that the developed model is a good classifier. The developed model based on unsupervised feature extraction and deep neural network is ready to be used on a large population before its installation for clinical usage.

scholarly journals Utilizing Indonesian Universal Language Model Fine-tuning for Text Classification

2021 ◽  
Vol 5 (3) ◽  
pp. 325
Author(s):  
Hendra Bunyamin
Keyword(s):  
Computer Vision ◽  
Natural Language Processing ◽  
Transfer Learning ◽  
Language Processing ◽  
Text Classification ◽  
Language Model ◽  
Fine Tuning ◽  
Classification Task ◽  
Universal Language ◽  
Learning Technique

Inductive transfer learning technique has made a huge impact on the computer vision field. Particularly, computer vision  applications including object detection, classification, and segmentation, are rarely trained from scratch; instead, they are fine-tuned from pretrained models, which are products of learning from huge datasets. In contrast to computer vision, state-of-the-art natural language processing models are still generally trained from the ground up. Accordingly, this research attempts to investigate an adoption of the transfer learning technique for natural language processing. Specifically, we utilize a transfer learning technique called Universal Language Model Fine-tuning (ULMFiT) for doing an Indonesian news text classification task. The dataset for constructing the language model is collected from several news providers from January to December 2017 whereas the dataset employed for text classification task comes from news articles provided by the Agency for the Assessment and Application of Technology (BPPT). To examine the impact of ULMFiT, we provide a baseline that is a vanilla neural network with two hidden layers. Although the performance of ULMFiT on validation set is lower than the one of our baseline, we find that the benefits of ULMFiT for the classification task significantly reduce the overfitting, that is the difference between train and validation accuracies from 4% to nearly zero.

Text Classification of Gujarati Newspaper Headlines

2021 ◽  
pp. 2050020
Author(s):  
Stuti Mehta ◽  
Suman K. Mitra
Keyword(s):  
Feature Extraction ◽  
Language Processing ◽  
Text Classification ◽  
Low Resource ◽  
Textual Data ◽  
Gujarati Language ◽  
News Headlines ◽  
Embedding Methods ◽  
Insight Into

Text classification is an extremely important area of Natural Language Processing (NLP). This paper studies various methods for embedding and classification in the Gujarati language. The dataset comprises of Gujarati News Headlines classified into various categories. Different embedding methods for Gujarati language and various classifiers are used to classify the headlines into given categories. Gujarati is a low resource language. This language is not commonly worked upon. This paper deals with one of the most important NLP tasks - classification and along with it, an idea about various embedding techniques for Gujarati language can be obtained since they help in feature extraction for the process of classification. This paper first performs embedding to get a valid representation of the textual data and then uses already existing robust classifiers to perform classification over the embedded data. Additionally, the paper provides an insight into how various NLP tasks can be performed over a low resource language like Gujarati. Finally, the research paper carries out a comparative analysis between the performances of various existing methods of embedding and classification to get an idea of which combination gives a better outcome.

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