A comprehensive method for attribute space extension for Random Forest

Adnan, Nasim; Islam, Zahidul

doi:10.1109/iccitechn.2014.7073129

Cited by 10 publications

(11 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…As noted in the previous works [1][2][3] the enrichment of feature space ensures significant contribution to the classification performance on the numeric data. The studies so far on extended space forests utilize either randomly chosen features [3] or the specific feature selection method such as gain ratio [1-2] to determine new candidate features to be consolidated to the original feature space.…”

Section: Word Embedding (We)mentioning

confidence: 80%

“…To get higher classification performance of ensemble system, they suggest utilizing the extended space methods. The recent studies [1][2] on extended space decision trees propose to increase the ensemble accuracy by suggesting another approach. Instead of randomly producing, new features with high classification capacity are generated by computing the gain ratio of each different candidate features.…”

Section: Related Workmentioning

confidence: 99%

“…After getting similarity vectors of d/2 number of randomly selected features, the best similarity score is chosen and divided into the total score of similarity vector to associate with the original feature space. This procedure mentioned above is repeated for all (1) where C represents the number of classes and P(C i ) demonstrates the probability of C i , P(t) and P(t') symbolizes the probability of presence and absence of term t', respectively.…”

Section: 4extended Feature Spacementioning

confidence: 99%

“…Significance level is set to 0.05 and the difference is accounted as statistically significant when the association of probability and Student's t-test is lower. The number of base learners is adjusted to 100 as represented in [1,3]. As we mentioned before, feature extension parameter is set to d number of features for all datasets for comparing experiment results with impressive work [3].…”

Section: Experiments Setupmentioning

confidence: 99%

See 3 more Smart Citations

Extended Feature Spaces Based Classifier Ensembles for Sentiment Analysis of Short Texts

Kilimci

Omurca

2018

ITC

View full text Add to dashboard Cite

Sentiment classification has become very popular to analyze opinions about events, products, and so on, especially for social networks such as Twitter. Due to the size limitation of expressing ideas on social networks, the classification performance needs to be boosted by proposing various techniques. In this work, the enhancement of feature space with word embedding based features is proposed to deal with the size limitation issues and the classification success of sentiment analysis is improved by employing classifier ensembles. The contributions of this paper are fivefold. First, the representative capabilities of features are enriched by using a semantic word embedding model and followingly the conventional feature selection techniques are compared. Second, traditional machine learning algorithms, namely naïve Bayes, support vector machine, and random forest are carried out to select baseline classifier for the proposed ensemble system. Third, three ensemble strategies namely, bagging, boosting, and random subspace are introduced to ensure the diversity of ensemble learning. Fourth, experiments are conducted to compare the performance of the models with the word embedding baseline. Eventually, a wide range of comparative experiments on Twitter datasets demonstrate that the classification performance of the proposed model significantly outperforms the state-of-the-art studies.

show abstract

Section: Word Embedding (We)mentioning

confidence: 80%

Section: Related Workmentioning

confidence: 99%

Section: 4extended Feature Spacementioning

confidence: 99%

Section: Experiments Setupmentioning

confidence: 99%

See 2 more Smart Citations

Extended Feature Spaces Based Classifier Ensembles for Sentiment Analysis of Short Texts

Kilimci

Omurca

2018

ITC

View full text Add to dashboard Cite

show abstract

“…Some of the most popular methods are naïve Bayes classifiers (NB), decision trees (DT), support vector machines (SVM), artificial neural networks (ANN), and k-nearest neighborhood classifiers (k-NN). Among these classifiers, decision trees have been extensively applied in the state of the art studies for ensemble learning [1][2][10][11][12][13][14]. Furthermore, usage of more than one decision tree emerges decision forests for ensemble learning.…”

Section: Introductionmentioning

confidence: 99%

The Impact of Enhanced Space Forests with Classifier Ensembles on Biomedical Dataset Classification

Kilimci¹

2018

ijisae

View full text Add to dashboard Cite

In this paper, we propose to advance the classification success of classifier ensembles by investigating the contribution of enhanced space forests. For this purpose, this study especially is focused on enhanced feature spaces by implementing the most popular feature selection techniques, namely information gain, and chi-square. After performing these methods on the original feature space, training phase is evaluated with all the original and the modified versions of most significant features, which are acquired by applying difference operator to the original features and the selected features with feature selection methods. That is, the new training dataset is constructed by combining the original features and the new ones. Then, the training is done with the well-known classification algorithm namely, decision tree using the enhanced feature space. Finally, three types of ensemble algorithms namely, bagging, random subspace, and random forest are carried out. A wide range of comparative experiments are conducted on publicly available and widely-used 36 datasets from the UCI machine learning repository to observe the impact of the enhanced space forests with classifier ensembles. Experiment results demonstrate that the proposed enhanced space forests perform better classification accuracy than the state of the art studies. Approximately, 1%-3% improvement of the classification success is an indicator that our proposed technique is efficient.

show abstract

A Novel Approach for Noisy Signal Classification Through the Use of Multiple Wavelets and Ensembles of Classifiers

Grant

Islam

2019

Advanced Data Mining and Applications

View full text Add to dashboard Cite

A comprehensive method for attribute space extension for Random Forest

Cited by 10 publications

References 14 publications

Extended Feature Spaces Based Classifier Ensembles for Sentiment Analysis of Short Texts

Extended Feature Spaces Based Classifier Ensembles for Sentiment Analysis of Short Texts

The Impact of Enhanced Space Forests with Classifier Ensembles on Biomedical Dataset Classification

A Novel Approach for Noisy Signal Classification Through the Use of Multiple Wavelets and Ensembles of Classifiers

Contact Info

Product

Resources

About