A Bayesian classifiers based combination model for automatic text classification

Rahman, Amna; Qamar, Usman

doi:10.1109/icsess.2016.7883016

Cited by 17 publications

(6 citation statements)

References 2 publications

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“…The advantages of Bayesian classification is its strong theoretical foundation and mathematical computation to make predictions, which makes it more transparent and easily accessible relative to other similar techniques. Furthermore, it may be used together with other classifiers to improve accuracy and performance for prediction [31], [32]. Bayesian classification employs Bayes theorem which is an algebraic model from fundamental of probability of hypothesis (H ) and evidence (E) is expressed in (1):…”

Section: E Intelligence Methodsmentioning

confidence: 99%

Investigation of Combined Electrical Modalities for Fault Diagnosis on a Wound-Rotor Induction Generator

Swana

Doorsamy

2019

IEEE Access

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An online method for fault diagnosis on a wound rotor induction generator using stator voltage and current, and rotor current are investigated. The diagnostic method comprises processing of the generators signals and classification of the machine's condition according to healthy or specific fault type. The signal processing phase of the intelligent fault diagnosis process extracts features, which are frequency-based, interrelated to specific fault modes, i.e., stator winding, rotor winding, and brush faults. Finite element modeling of a wound-rotor induction generator is carried out under normal and different fault conditions for the purpose of conducting preliminary design and testing of the classification system. An experimental setup is then used to validate the computational results and verify the diagnostic method. The results indicate that the stator voltage, stator current, and rotor current modalities exhibit patterned sensitivities to the investigated faults. It is found that the classifier works well with a large number of features offered by the combination of these modalities yielding a best overall diagnostic accuracy of 99% in an experimental setting. INDEX TERMS Condition monitoring, wound-rotor induction generator, fault diagnosis, classification.

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Section: E Intelligence Methodsmentioning

confidence: 99%

Investigation of Combined Electrical Modalities for Fault Diagnosis on a Wound-Rotor Induction Generator

Swana

Doorsamy

2019

IEEE Access

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“…The datasets were Reuters-REO, Review Polarity, and Reuters-REI. The proposed method outperformed on six classifiers (BN, KNN, MNB, SVM, NV, and decision tree) (Rahman and Usman, 2016).…”

Section: Related Workmentioning

confidence: 96%

Improving Kurdish Web Mining through Tree Data Structure and Porter’s Stemmer Algorithms

et al. 2018

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A B S T R A C TStemming is one of the main important preprocessing techniques that can be used to enhance the accuracy of text classification. The key purpose of using the stemming is combining the number of words that have the same stem to decrease high dimensionality of feature space. Reducing feature space causes to decline time to construct a model and minimize the memory space. In this paper, a new stemming approach is explored for enhancing Kurdish text classification performance. Tree data structure and Porter's stemmer algorithms are incorporated for building the proposed approach. The system is assessed through using support vector machine and decision tree (C4.5) to illustrate the performance of the suggested stemmer after and before applying it. Furthermore, the usefulness of using stop words is considered before and after implementing the suggested approach.

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“…In addition, Text classification is an activity that labels natural language text with predefined categories [32]. It requires cross-disciplinary knowledge to build models and to improve accuracy of prediction [33].…”

Section: B Text Mining and Text Classificationmentioning

confidence: 99%

Classification of Proactive Personality: Text Mining Based on Weibo Text and Short-Answer Questions Text

Wang

Yun

et al. 2020

IEEE Access

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This study focused on the topic of predicting ''proactive personality''. With 901 participants selected by cluster sampling method, targeted short-answer questions text and participants' social media post text (Weibo) were obtained while participants' labels of proactive personality were evaluated by experts. In order to make classification, five machine learning algorithms included Support Vector Machine (SVM), XGBoost, K-Nearest-Neighbors (KNN), Naive Bayes (NB) and Logistic Regression (LR) were deployed. Seven different indicators, which include Accuracy (ACC), F1-score (F1), Sensitivity (SEN), Specificity (SPE), Positive Predictive Value (PPV), Negative Predictive Value (NPV) and Area under Curve (AUC), combined with hierarchical cross-validation were also used to make the comprehensive evaluation of models. With participants' Weibo text and short-answer questions text, we proposed a new approach to classify individuals' proactive personality based on text mining technology. The results showed that short-answer questions + Weibo text datasets had the best performance, followed by short-answer questions text datasets, while the outcome of Weibo text datasets were the worst. However, it is noteworthy that Weibo text has the highest average score on the SPE, which indicated that Weibo text played an important role in identifying individuals with low proactive personality. With Weibo text, SEN was also improved compared with only applying short-answer questions text. In addition, among all three datasets, the indicator SPE is always higher than SEN, indicating this text classification approach was more competent for identifying college students with low proactive personality. As for algorithms, Support Vector Machine and Logistic Regression showed steadier performance compared with other algorithms.INDEX TERMS Machine learning, proactive personality, text mining.

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A Bayesian classifiers based combination model for automatic text classification

Cited by 17 publications

References 2 publications

Investigation of Combined Electrical Modalities for Fault Diagnosis on a Wound-Rotor Induction Generator

Investigation of Combined Electrical Modalities for Fault Diagnosis on a Wound-Rotor Induction Generator

Improving Kurdish Web Mining through Tree Data Structure and Porter’s Stemmer Algorithms

Classification of Proactive Personality: Text Mining Based on Weibo Text and Short-Answer Questions Text

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