Combining multiple classifiers using vote based classifier ensemble technique for named entity recognition

Saha, Sriparna; Ekbal, Asif

doi:10.1016/j.datak.2012.06.003

Cited by 103 publications

(51 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This is an intermediate result. In our second setting we combine all the solutions of the final Pareto optimal front using a MOO based classifier ensemble technique [24]. This is done to further improve the performance as well as to compare with the last two baselines (i.e.…”

Section: Selection Of a Single Solution From Pareto Optimal Frontmentioning

confidence: 99%

“…Thus it is very difficult to select a unique solution from the best population. Hence rather than selecting a solution we combine all the outputs of the classifiers using a MOO based classifier ensemble technique [24].…”

Section: Combining Solutions Of the Final Populationmentioning

confidence: 99%

“…Similarly after the application of MOO based technique we obtain a set of solutions on the final Pareto optimal front for CRF and SVM each. We combine the outputs of these solutions using a MOO based classifier ensemble technique proposed in [24]. The methods are evaluated on the benchmark setup of patent and Medline datasets.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Joint model for feature selection and parameter optimization coupled with classifier ensemble in chemical mention recognition

Ekbal

Saha

2015

Knowledge-Based Systems

Self Cite

View full text Add to dashboard Cite

Section: Selection Of a Single Solution From Pareto Optimal Frontmentioning

confidence: 99%

Section: Combining Solutions Of the Final Populationmentioning

confidence: 99%

See 1 more Smart Citation

Joint model for feature selection and parameter optimization coupled with classifier ensemble in chemical mention recognition

Ekbal

Saha

2015

Knowledge-Based Systems

Self Cite

View full text Add to dashboard Cite

“…Our motivations are two-fold. First, [13] showed that MOO strategies evidence improved results when compared to single objective solutions and state-of-the-art baselines. Second, MOO techniques propose a set of performing solutions rather than a single one.…”

Section: Learning Frameworkmentioning

confidence: 99%

“…Indeed, due to the small size of training data (only 100 queries distributed equally by class), classification results are weak if a single classifier is used in a traditional supervised way. To overcome this situation, we follow the ensemble learning paradigm defined as a multi-objective optimization problem in a similar way as [13].…”

Section: Related Workmentioning

confidence: 99%

Understanding Temporal Query Intent

Hasanuzzaman

Saha

Dias

et al. 2015

Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval

Self Cite

View full text Add to dashboard Cite

Understanding the temporal orientation of web search queries is an important issue for the success of information access systems. In this paper, we propose a multi-objective ensemble learning solution that (1) allows to accurately classify queries along their temporal intent and (2) identifies a set of performing solutions thus offering a wide range of possible applications. Experiments show that correct representation of the problem can lead to great classification improvements when compared to recent state-of-the-art solutions and baseline ensemble techniques.

show abstract

Computational methods for predicting hotspots at protein–RNA interfaces

2021

View full text Add to dashboard Cite

Protein-RNA interactions play essential roles in many critical biological events. A comprehensive understanding of the mechanisms underlying these interactions is helpful when studying cellular activities and therapeutic applications. Hotspots are a small portion of residues contributing much toward protein-RNA binding affinity. In pharmaceutical research, the hotspot residues are seen as the best option for designing small molecules to target proteins of therapeutic interest. With the accumulation of experimental data about protein-RNA interactions, computational methods have been producedfor hotspot prediction on a large scale. In this review, we first present an overview of the existing databases for protein-RNA binding data. Furthermore, we outline the most adopted computational methods for hotspots prediction in protein-RNA interactions. Finally, we discuss the applications of hotspot prediction.

show abstract

Combining multiple classifiers using vote based classifier ensemble technique for named entity recognition

Cited by 103 publications

References 13 publications

Joint model for feature selection and parameter optimization coupled with classifier ensemble in chemical mention recognition

Joint model for feature selection and parameter optimization coupled with classifier ensemble in chemical mention recognition

Understanding Temporal Query Intent

Computational methods for predicting hotspots at protein–RNA interfaces

Contact Info

Product

Resources

About