Feature selection for multi-label text data: An ensemble approach using geometric mean aggregation

Miri, Mohsen; Dowlatshahi, Mohammad Bagher; Hashemi, Amin

doi:10.1109/cfis54774.2022.9756484

Cited by 5 publications

(3 citation statements)

References 23 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…An ensemble-based feature selection technique for multi-label text data is presented in the [51]. This novel method improves multi-label classification model performance by efficiently identifying the most informative characteristics necessary for precise label prediction.…”

Section: Plos Onementioning

confidence: 99%

“…• BERT [51]: A transformational NLP approach called Bidirectional Encoder Representations from Transformers (BERT) looks at the complete phrase from both directions to collect contextual word representations. Using an extensive directional Transformer architecture, it is pre-trained on next-sentence prediction tasks and masked language modeling on unlabeled text.…”

Section: Plos Onementioning

confidence: 99%

See 1 more Smart Citation

Enhancing aspect-based multi-labeling with ensemble learning for ethical logistics

Almazroi,

Ayub

2024

PLoS ONE

View full text Add to dashboard Cite

In the dynamic domain of logistics, effective communication is essential for streamlined operations. Our innovative solution, the Multi-Labeling Ensemble (MLEn), tackles the intricate task of extracting multi-labeled data, employing advanced techniques for accurate preprocessing of textual data through the NLTK toolkit. This approach is carefully tailored to the prevailing language used in logistics communication. MLEn utilizes innovative methods, including sentiment intensity analysis, Word2Vec, and Doc2Vec, ensuring comprehensive feature extraction. This proves particularly suitable for logistics in e-commerce, capturing nuanced communication essential for efficient operations. Ethical considerations are a cornerstone in logistics communication, and MLEn plays a pivotal role in detecting and categorizing inappropriate language, aligning inherently with ethical norms. Leveraging Tf-IDF and Vader for feature enhancement, MLEn adeptly discerns and labels ethically sensitive content in logistics communication. Across diverse datasets, including Emotions, MLEn consistently achieves impressive accuracy levels ranging from 92% to 97%, establishing its superiority in the logistics context. Particularly, our proposed method, DenseNet-EHO, outperforms BERT by 8% and surpasses other techniques by a 15-25% efficiency. A comprehensive analysis, considering metrics such as precision, recall, F1-score, Ranking Loss, Jaccard Similarity, AUC-ROC, sensitivity, and time complexity, underscores DenseNet-EHO’s efficiency, aligning with the practical demands within the logistics track. Our research significantly contributes to enhancing precision, diversity, and computational efficiency in aspect-based sentiment analysis within logistics. By integrating cutting-edge preprocessing, sentiment intensity analysis, and vectorization, MLEn emerges as a robust framework for multi-label datasets, consistently outperforming conventional approaches and giving outstanding precision, accuracy, and efficiency in the logistics field.

show abstract

Section: Plos Onementioning

confidence: 99%

Section: Plos Onementioning

confidence: 99%

Enhancing aspect-based multi-labeling with ensemble learning for ethical logistics

Almazroi,

Ayub

2024

PLoS ONE

View full text Add to dashboard Cite

show abstract

“…This approach applies three different individual feature selection methods to determine feature grades, while using type I fuzzy to handle feature selection uncertainty and reduce noise, thereby improving accuracy, precision, and recall rates. Miri, Dowlatshahi & Hashemi (2022) presented an integrated multi-label feature selection method called GMA, based on geometric mean aggregation of text datasets. This method utilizes four different structures of multi-label feature selection algorithms and has demonstrated excellent results on high-dimensional text data.…”

Section: Introductionmentioning

confidence: 99%

An ensemble learning-based feature selection algorithm for identification of biomarkers of renal cell carcinoma

Xin,

Lv,

Liu

et al. 2024

PeerJ Computer Science

View full text Add to dashboard Cite

Feature selection plays a crucial role in classification tasks as part of the data preprocessing process. Effective feature selection can improve the robustness and interpretability of learning algorithms, and accelerate model learning. However, traditional statistical methods for feature selection are no longer practical in the context of high-dimensional data due to the computationally complex. Ensemble learning, a prominent learning method in machine learning, has demonstrated exceptional performance, particularly in classification problems. To address the issue, we propose a three-stage feature selection algorithm framework for high-dimensional data based on ensemble learning (EFS-GINI). Firstly, highly linearly correlated features are eliminated using the Spearman coefficient. Then, a feature selector based on the F-test is employed for the first stage selection. For the second stage, four feature subsets are formed using mutual information (MI), ReliefF, SURF, and SURF* filters in parallel. The third stage involves feature selection using a combinator based on GINI coefficient. Finally, a soft voting approach is proposed to employ for classification, including decision tree, naive Bayes, support vector machine (SVM), k-nearest neighbors (KNN) and random forest classifiers. To demonstrate the effectiveness and efficiency of the proposed algorithm, eight high-dimensional datasets are used and five feature selection methods are employed to compare with our proposed algorithm. Experimental results show that our method effectively enhances the accuracy and speed of feature selection. Moreover, to explore the biological significance of the proposed algorithm, we apply it on the renal cell carcinoma dataset GSE40435 from the Gene Expression Omnibus database. Two feature genes, NOP2 and NSUN5, are selected by our proposed algorithm. They are directly involved in regulating m5c RNA modification, which reveals the biological importance of EFS-GINI. Through bioinformatics analysis, we shows that m5C-related genes play an important role in the occurrence and progression of renal cell carcinoma, and are expected to become an important marker to predict the prognosis of patients.

show abstract

Ensemble Multi-label Feature Selection Using Weighted Harmonic Mean

Mohanrasu,

Rakkiyappan

2024

Lecture Notes in Networks and Systems

View full text Add to dashboard Cite

Feature selection for multi-label text data: An ensemble approach using geometric mean aggregation

Cited by 5 publications

References 23 publications

Enhancing aspect-based multi-labeling with ensemble learning for ethical logistics

Enhancing aspect-based multi-labeling with ensemble learning for ethical logistics

An ensemble learning-based feature selection algorithm for identification of biomarkers of renal cell carcinoma

Ensemble Multi-label Feature Selection Using Weighted Harmonic Mean

Contact Info

Product

Resources

About