An Ensemble-Based Feature Selection and Classification of Gene Expression using Support Vector Machine, K-Nearest Neighbor, Decision Tree

Nair, Anu J; Rasheed, Rizwana; Maheeshma, KM; Aiswarya, L. S.; Kavitha, K.

doi:10.1109/icces45898.2019.9002041

Cited by 17 publications

(1 citation statement)

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“…Numerous studies have demonstrated the superiority of ensemble techniques over individual classifiers in various domains. For example, in a study on the classification of gene expression data, an ensemble of support vector machines (SVMs) outperformed individual SVMs and other popular classification methods, achieving higher accuracy and stability across different datasets [25]. Another study, on land use/land cover classification, showed that a hybrid ensemble of decision trees and SVMs achieved higher accuracy than individual classifiers and other ensemble methods [26].…”

Section: Introductionmentioning

confidence: 99%

An Ensemble-Learning-Based Technique for Bimodal Sentiment Analysis

Shah

Ghomeshi

Vakaj

et al. 2023

BDCC

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Human communication is predominantly expressed through speech and writing, which are powerful mediums for conveying thoughts and opinions. Researchers have been studying the analysis of human sentiments for a long time, including the emerging area of bimodal sentiment analysis in natural language processing (NLP). Bimodal sentiment analysis has gained attention in various areas such as social opinion mining, healthcare, banking, and more. However, there is a limited amount of research on bimodal conversational sentiment analysis, which is challenging due to the complex nature of how humans express sentiment cues across different modalities. To address this gap in research, a comparison of multiple data modality models has been conducted on the widely used MELD dataset, which serves as a benchmark for sentiment analysis in the research community. The results show the effectiveness of combining acoustic and linguistic representations using a proposed neural-network-based ensemble learning technique over six transformer and deep-learning-based models, achieving state-of-the-art accuracy.

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Section: Introductionmentioning

confidence: 99%