An Improved ID3 Classification Algorithm Based On Correlation Function and Weighted Attribute

Es-sabery, Fatima; Haïr, Abdellatif

doi:10.1109/isacs48493.2019.9068891

Cited by 10 publications

(19 citation statements)

References 6 publications

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“…This metric is calculated by using (11). Where Precision is computed using the formula (9), and Recall is calculated using the formula (4).…”

Section: F Alsen Egativeratementioning

confidence: 99%

“…Such as NLP methods for data pre-processing like word stemming, word lemmatization, and effect of negation. Machine Learning (ML) methods for sentiment classification like Support Vector Machines (SVM) [4], K-Nearest Neighbors (KNN) [5], Random Forest (RF) [6], Logistic Regression (LR) [7], Naive Bayesian (NB) [8] or Decision Tree (DT) classifier [9]. Deep Learning (DL) methods like Convolutional Neural Network (CNN) [10], Feedforward Neural Network (FFNN) [11], Long Short-Term Memory (LSTM) [12], Gated Recurrent Unit (GRU) [13], or Recurrent Neural Network (RNN) [14] which are used for automatically extracting features from the given text and for performing the sentiment classification.…”

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confidence: 99%

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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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“…This metric is calculated by using (11). Where Precision is computed using the formula (9), and Recall is calculated using the formula (4).…”

Section: F Alsen Egativeratementioning

confidence: 99%

mentioning

confidence: 99%

Sentence-Level Classification Using Parallel Fuzzy Deep Learning Classifier

et al. 2021

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“…(y j ) is the class degree (CD) of the ith fuzzy set of the kth attribute A (k) i with respect to the jth fuzzy class y j calculated using Equation (2). Furthermore, the fuzzy entropy (FE) of the kth attribute A (k) is defined as a weighted sum of the FE A (k)…”

Section: Parallel Fuzzy C45 Decision Tree Algorithmmentioning

confidence: 99%

“…The historical data for a classification project is characteristically split into two data sets: one for building the model; the other for testing the model. The most used classification algorithm is the decision tree algorithm [2].…”

Section: Introductionmentioning

confidence: 99%

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

Es-sabery

Haïr

2019

Fuzzy Information and Engineering

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Decision tree is the most efficient and fast technology of data mining that is frequently used in data analysis and prediction. According to the development in science and technology in the last years, the data is growing faster, and the principle of the decision tree algorithms become not efficient in respect runtime and speed-up ratio. In view of the above problem, we propose a new method of classification based on framework Hadoop and Fuzzy logic. Our proposed hybrid approach is designed to propose a new C4.5 decision tree algorithm using fuzzy logic and fuzzy set theory to handle uncertainty and imprecision in data, and Hadoop framework (MapReduce + HDFS) to parallelize our work. This combination of big data technologies, fuzzy systems and C4.5 decision tree algorithm has produced a parallel fuzzy decision tree model, which takes advantage of these three techniques (hadoop + fuzzy logic + C4.5) to produce a decision tree with higher predictive accuracy. In this paper, an experiment is presented to compare our approach with other approaches from the literature. Experiments were carried out using three datasets, and the results show that our new method outperforms the other approaches in terms of accuracy and execution time.

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A MapReduce Improved ID3 Decision Tree for Classifying Twitter Data

Es-sabery

Haïr

2021

Business Intelligence

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An Improved ID3 Classification Algorithm Based On Correlation Function and Weighted Attribute

Cited by 10 publications

References 6 publications

Sentence-Level Classification Using Parallel Fuzzy Deep Learning Classifier

Sentence-Level Classification Using Parallel Fuzzy Deep Learning Classifier

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

A MapReduce Improved ID3 Decision Tree for Classifying Twitter Data

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