A MapReduce C4.5 Decision Tree Algorithm Based on Fuzzy Rule-Based System

Es-sabery, Fatima; Haïr, Abdellatif

doi:10.1080/16168658.2020.1756099

Cited by 14 publications

(11 citation statements)

References 35 publications

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“…To assess our text classification method, we principally compute ten assessment metrics: True Positive Rate (TPR), True Negative Rate (TNR), Kappa Statistic (KS), False Positive Rate (FPR), Precision (PR), False Negative Rate (FNR), Classification Rate or Accuracy (AC), Error Rate (ER), Time Consumption (TC), and F1-score (FS) [103]. These evaluation metrics are computed as described in Table 7 and based on the confusion matrix for binary classification [68] as given in Fig.…”

Section: A Evaluation Metricsmentioning

confidence: 99%

A MapReduce Opinion Mining for COVID-19-Related Tweets Classification Using Enhanced ID3 Decision Tree Classifier

Es-sabery

Qadir

et al. 2021

IEEE Access

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Opinion Mining (OM) is a field of Natural Language Processing (NLP) that aims to capture human sentiment in the given text. With the ever-spreading of online purchasing websites, micro-blogging sites, and social media platforms, OM in online social media platforms has picked the interest of thousands of scientific researchers. Because the reviews, tweets and blogs acquired from these social media networks, act as a significant source for enhancing the decision making process. The obtained textual data (reviews, tweets, or blogs) are classified into three different class labels which are negative, neutral and positive for analyzing and extracting relevant information from the given dataset. In this contribution, we introduce an innovative MapReduce improved weighted ID3 decision tree classification approach for OM, which consists mainly of three aspects: Firstly We have used several feature extractors to efficiently detect and capture the relevant data from the given tweets, including N-grams or character-level, Bag-Of-Words, word embedding (GloVe, Word2Vec), FastText, and TF-IDF. Secondly, we have applied a multiple feature selector to reduce the high feature's dimensionality, including Chi-square, Gain Ratio, Information Gain, and Gini Index. Finally, we have employed the obtained features to carry out the classification task using an improved ID3 decision tree classifier, which aims to calculate the weighted information gain instead of information gain used in traditional ID3. In other words, to measure the weighted information gain for the current conditioned feature, we follow two steps: First, we compute the weighted correlation function of the current conditioned feature. Second, we multiply the obtained weighted correlation function by the information gain of this current conditioned feature. This work is implemented in a distributed environment using the Hadoop framework, with its programming framework MapReduce and its distributed file system HDFS. Its primary goal is to enhance the performance of a well-known ID3 classifier in terms of accuracy, execution time, and ability to handle the massive datasets. We have carried out several experiences that aims to assess the effectiveness of our suggested classifier compared to some other contributions chosen from the literature. The experimental results demonstrated that our ID3 classifier works better on COVID-19_Sentiments dataset than other classifiers in terms of Recall (85.72 %), specificity (86.51 %), error rate (11.18 %), false-positive rate (13.49 %), execution time (15.95s), kappa statistic (87.69 %), F1-score (85.54 %), classification rate (88.82 %), false-negative rate (14.28 %), precision rate (86.67 %), convergence (it convergent towards the iteration 90), stability (it is more stable with mean deviation standard equal to 0.12 %), and complexity (it requires much lower time and space computational complexity).

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Section: A Evaluation Metricsmentioning

confidence: 99%

A MapReduce Opinion Mining for COVID-19-Related Tweets Classification Using Enhanced ID3 Decision Tree Classifier

Es-sabery

Qadir

et al. 2021

IEEE Access

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“…In the literature, there are three well-known FISs, which are Mamdani, Sugeno, and Tsukamoto. Both Tsukamoto and Sugeno systems are applied in the case of regression issues, unlike the Mamdani, which is utilized in the case of system classification problems [28]. The major distinction between the MFS and the Sugeno fuzzy system lies in the manner that each system defines the consequent block of its fuzzy If-Then rules.…”

Section: Mamdani Fuzzy Systemmentioning

confidence: 99%

“…Based on the Fuzzy set theory, multiple fuzzy systems are proposed. We find amongst Mamdani, Tsukamoto, and Sugeno fuzzy system [28]. Both later systems are applied in the regression problem, but the Mamdani is used in the classification issue.…”

Section: G Phase Vi: Mamdani Fuzzy Systemmentioning

confidence: 99%

“…i.e., in fuzzy logic theory, the linguistic variable takes a real value between 0 and 1 instead of, in deterministic logic, each variable takes either 0 or 1. The principal purpose of the fuzzy logic theory is transforming a white and black issue into a grey problem [28]. In the descriptions of set theory, deterministic or conventional logic assumes that the set of linguistic variables as the crisp linguistic variables, which signifies that each linguistic variable's measured membership degree in the crisp collection is equal to 1.…”

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Sentence-Level Classification Using Parallel Fuzzy Deep Learning Classifier

et al. 2021

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At present, with the growing number of Web 2.0 platforms such as Instagram, Facebook, and Twitter, users honestly communicate their opinions and ideas about events, services, and products. Owing to this rise in the number of social platforms and their extensive use by people, enormous amounts of data are produced hourly. However, sentiment analysis or opinion mining is considered as a useful tool that aims to extract the emotion and attitude from the user-posted data on social media platforms by using different computational methods to linguistic terms and various Natural Language Processing (NLP). Therefore, enhancing text sentiment classification accuracy has become feasible, and an interesting research area for many community researchers. In this study, a new Fuzzy Deep Learning Classifier (FDLC) is suggested for improving the performance of data-sentiment classification. Our proposed FDLC integrates Convolutional Neural Network (CNN) to build an effective automatic process for extracting the features from collected unstructured data and Feedforward Neural Network (FFNN) to compute both positive and negative sentimental scores. Then, we used the Mamdani Fuzzy System (MFS) as a fuzzy classifier to classify the outcomes of the two used deep (CNN+FFNN) learning models in three classes, which are: Neutral, Negative, and Positive. Also, to prevent the long execution time taking by our hybrid proposed FDLC, we have implemented our proposal under the Hadoop cluster. An experimental comparative study between our FDLC and some other suggestions from the literature is performed to demonstrate our offered classifier's effectiveness. The empirical result proved that our FDLC performs better than other classifiers in terms of true positive rate, true negative rate, false positive rate, false negative rate, error rate, precision, classification rate, kappa statistic, F1-score and time consumption, complexity, convergence, and stability.

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“…Although applying the most efficient machine learning and deep learning approaches, NLP's inherent vagueness requires more solutions. Numerous works from the literature [3], [4][5] prove that fuzzy logic theories are the appropriate techniques to handle ambiguous, uncertain and imprecise information.…”

Section: Introductionmentioning

confidence: 99%

Sentiment Analysis of Covid19 Tweets Using A MapReduce Fuzzified Hybrid Classifier Based On C4.5 Decision Tree and Convolutional Neural Network

Es-sabery

Garmani

et al. 2021

E3S Web Conf.

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This contribution proposes a new model for sentiment analysis, which combines the convolutional neural network (CNN), C4.5 decision tree algorithm, and Fuzzy Rule-Based System (FRBS). Our suggested method consists of six parts. Firstly we have applied several pre-processing techniques. Secondly, we have used the fastText method for vectoring the analysed tweets. Thirdly, we have implemented the CNN for extracting and selecting the pertinent features from the tweets. Fourthly, we have fuzzified the CNN output using the Gaussian Fuzzification (GF) method for coping with vague data. Then we have applied fuzziness C4.5 for creating the fuzziness rules. Finally, we have used the General Fuzziness Reasoning (GFR) approach for classifying the new tweets. In summary, our method integrates the advantages of CNN and C4.5 techniques and overcomes the shortcomings of ambiguous data in the tweets using FRBS, which is consists of three-phase: fuzzification phase using GF, inference mechanism using fuzziness C4.5, and defuzzification phase using GFR. Also, to give our approach the ability to deal with the massive data, we have implemented it on the Hadoop framework of five computers. The experiential findings confirmed that our model operates excellently compared to other chosen models form the literature.

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A MapReduce C4.5 Decision Tree Algorithm Based on Fuzzy Rule-Based System

Cited by 14 publications

References 35 publications

A MapReduce Opinion Mining for COVID-19-Related Tweets Classification Using Enhanced ID3 Decision Tree Classifier

A MapReduce Opinion Mining for COVID-19-Related Tweets Classification Using Enhanced ID3 Decision Tree Classifier

Sentence-Level Classification Using Parallel Fuzzy Deep Learning Classifier

Sentiment Analysis of Covid19 Tweets Using A MapReduce Fuzzified Hybrid Classifier Based On C4.5 Decision Tree and Convolutional Neural Network

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