Fuzzy fast classification algorithm with hybrid of ID3 and SVM

Srinivasan, V.; Rajenderan, G.; Kuzhali, J. Vandar; Aruna, M.

doi:10.3233/ifs-2012-0574

Cited by 6 publications

(5 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…There are many works concerning the ID3 decision tree algorithm and their improvements like Es-Sabery et al [33]; Yu-Xun et al [41]; Chai et al [42]; Elyassami et al [43]; Zou et al [44]; Srinivasan et al [45]; Chen et al [46]; Ding et al [47]; Zhu et al [48]; Kaewrod et al [49]; Rajeshkanna et al [50]; and as introduced in the Table 3 and 4. In [33], we proposed a novel enhanced ID3 decision tree algorithm, which integrates the weighted theory and information gain criterion.…”

Section: Previous Researchmentioning

confidence: 99%

“…The utilization of ID3 proved that this algorithm could perform the data classification very well, and it supplies decision-makers with a set of decision rules. Srinivasan et al [45] designed the Fast Fuzzy classification method for getting better performances of classification. They also have incorporated the advantages of the ID3 decision tree and the SVM algorithm, for improving the accuracy and for getting a fast classification result.…”

Section: Previous Researchmentioning

confidence: 99%

See 1 more Smart Citation

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

Es-sabery

Qadir

et al. 2021

IEEE Access

View full text Add to dashboard Cite

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).

show abstract

Section: Previous Researchmentioning

confidence: 99%

Section: Previous Researchmentioning

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

View full text Add to dashboard Cite

show abstract

“…Linear Model: the classification surface equation of SVM is given in equation 1: dividing line or plane. So they are called as Support Vector (SV) [4].…”

Section: Support Vector Machinementioning

confidence: 99%

“…Introducing nuclear methods: mapping the data in a low dimensional data space into a high dimensional feature space, the classification problem is transformed into the feature space. Vector dot product operations in feature space are corresponding with kernel functions in data space.Through introducing ( ) ( ) equation(3)could be transformed into equation(4).…”

mentioning

confidence: 99%

An Unbalanced Penalty SVM for Fault Identification of BOSS

Chen¹,

Peng²,

Li³

2013

Proceedings of the 2nd International Conference on Management Science and Industrial Engineering (MSIE 2013)

View full text Add to dashboard Cite

In order to solve classification error problems of support vector machine, which was used in the telecommunication business supporting system (BOSS), caused by the unbalanced ratio of positive samples, which stand for proper states of BOSS, and negative samples, which stand for the improper states, an unbalanced penalty SVM algorithm was proposed. In the proposed algorithm, values of the penalties were inverse to the ratio of the numbers of positive and negative samples, which means that the number of samples is higher, the lower the penalty coefficient. At last, in order to prove the effectiveness of the proposed algorithm, an experiment was conducted on the classification of running data of BOSS. The result of the experiment proved that the proposed SVM algorithm greatly reduces the negative sample misclassification when the ratio of positive and negative samples was not balanced, which proved the validity of the proposed algorithm.

show abstract

“…Liu and Xue [13] focused on designing a new class of kernels to incorporate fuzzy prior information into the training process of SVRs. Currently, SVMs have received extensive attention and are attracting more and more scholars to study from different views [14][15][16][17][18][19][20][21][22][23][24][25][26][27][28][29].…”

Section: Introductionmentioning

confidence: 99%

Priori Information Based Support Vector Regression and Its Applications

Chen

2015

Mathematical Problems in Engineering

View full text Add to dashboard Cite

In order to extract the priori information (PI) provided by real monitored values of peak particle velocity (PPV) and increase the prediction accuracy of PPV, PI based support vector regression (SVR) is established. Firstly, to extract the PI provided by monitored data from the aspect of mathematics, the probability density of PPV is estimated withε-SVR. Secondly, in order to make full use of the PI about fluctuation of PPV between the maximal value and the minimal value in a certain period of time, probability density estimated withε-SVR is incorporated into training data, and then the dimensionality of training data is increased. Thirdly, using the training data with a higher dimension, a method of predicting PPV called PI-ε-SVR is proposed. Finally, with the collected values of PPV induced by underwater blasting at Dajin Island in Taishan nuclear power station in China, contrastive experiments are made to show the effectiveness of the proposed method.

show abstract

Fuzzy fast classification algorithm with hybrid of ID3 and SVM

Cited by 6 publications

References 9 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

An Unbalanced Penalty SVM for Fault Identification of BOSS

Priori Information Based Support Vector Regression and Its Applications

Contact Info

Product

Resources

About