Feature Selection using Particle Swarm Optimization for Thermal Face Recognition

Seal, Ayan; Ganguly, Suranjan; Bhattacharjee, Debotosh; Nasipuri, Mita; Gonzalo-Martín, Consuelo

doi:10.1007/978-81-322-1985-9_2

Cited by 19 publications

(12 citation statements)

References 16 publications

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“…Here we propose a PSO based feature selection technique that determines the most relevant features from a full word embedding set, and use this subset as feature for classifier's training. Feature selection has been widely used for many tasks such as gene expression (Ding and Peng, 2005), face recognition (Seal et al, 2015) and signal processing (Alamedine et al, 2013). Dealing with biomedical text is, however, more difficult and challenging as the features have non-numeric values and the texts are heavily unstructured.…”

Section: Related Workmentioning

confidence: 99%

Entity Extraction in Biomedical Corpora: An Approach to Evaluate Word Embedding Features with PSO based Feature Selection

Yadav¹,

Ekbal²,

Saha³

et al. 2017

Proceedings of the 15th Conference of the European Chapter of The Association for Computational Linguistics: Volume 1

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Text mining has drawn significant attention in recent past due to the rapid growth in biomedical and clinical records. Entity extraction is one of the fundamental components for biomedical text mining. In this paper, we propose a novel approach of feature selection for entity extraction that exploits the concept of deep learning and Particle Swarm Optimization (PSO). The system utilizes word embedding features along with several other features extracted by studying the properties of the datasets. We obtain an interesting observation that compact word embedding features as determined by PSO are more effective compared to the entire word embedding feature set for entity extraction. The proposed system is evaluated on three benchmark biomedical datasets such as GENIA, GENETAG and AiMed. The effectiveness of the proposed approach is evident with significant performance gains over the baseline models as well as the other existing systems. We observe improvements of 7.86%, 5.27% and 7.25% F-measure points over the baseline models for GE-NIA, GENETAG, and AiMed dataset respectively.

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Section: Related Workmentioning

confidence: 99%

Entity Extraction in Biomedical Corpora: An Approach to Evaluate Word Embedding Features with PSO based Feature Selection

Yadav¹,

Ekbal²,

Saha³

et al. 2017

Proceedings of the 15th Conference of the European Chapter of The Association for Computational Linguistics: Volume 1

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show abstract

“…e particles in a group have a certain size, and each particle has two characteristics, namely, location and velocity. PSO is a well-known tool for finding the optimal characteristics of a particle by performing local and global iterative searches in the feature search space [11]. In PSO, there is a group of random particles that moves around the solution space until convergence is reached.…”

Section: Particle Swarm Optimisationmentioning

confidence: 99%

“…In PSO, there is a group of random particles that moves around the solution space until convergence is reached. ere are some features that are irrelevant and noisy and lead to high misclassification rates [11].…”

Section: Particle Swarm Optimisationmentioning

confidence: 99%

“…For each particle, the pbest is the particle position with the best fitness that has been obtained by each particle so far. After determining pbest and gbest, the positions and velocities will be updated as specified by Seal and colleagues [11]:…”

Section: Particle Swarm Optimisationmentioning

confidence: 99%

“…PSO is one of the tools for feature selection. In a previous study, Seal et al researched the use of PSO for feature selection in thermal face recognition [11]. Cases that used PSO provided markedly better performance results than those that did not use PSO; this inspired the author to try it in predicting the stock price model.…”

Section: Data Preprocessingmentioning

confidence: 99%

See 2 more Smart Citations

Application of Support Vector Regression in Indonesian Stock Price Prediction with Feature Selection Using Particle Swarm Optimisation

Rustam

Kintandani

2019

Modelling and Simulation in Engineering

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Stock investing is one of the most popular types of investments since it provides the highest return among all investment types; however, it is also associated with considerable risk. Fluctuating stock prices provide an opportunity for investors to make a high profit. We can see the movement of groups of stock prices from the stock index, which is called Jakarta Composite Index (JKSE) in Indonesia. Several studies have focused on the prediction of stock prices using machine learning, while one uses support vector regression (SVR). Therefore, this study examines the application of SVR and particle swarm optimisation (PSO) in predicting stock prices using stock historical data and several technical indicators, which are selected using PSO. Subsequently, a support vector machine (SVM) was applied to predict stock prices with the technical indicator selected by PSO as the predictor. The study found that stock price prediction using SVR and PSO shows good performances for all data, and many features and training data used by the study have relatively low error probabilities. Thereby, an accurate model was obtained to predict stock prices in Indonesia.

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Plant Leaf Disease Detection and Classification Using Particle Swarm Optimization

Yadav¹,

Rana²,

Nagpal

2019

Machine Learning for Networking

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Feature Selection using Particle Swarm Optimization for Thermal Face Recognition

Cited by 19 publications

References 16 publications

Entity Extraction in Biomedical Corpora: An Approach to Evaluate Word Embedding Features with PSO based Feature Selection

Entity Extraction in Biomedical Corpora: An Approach to Evaluate Word Embedding Features with PSO based Feature Selection

Application of Support Vector Regression in Indonesian Stock Price Prediction with Feature Selection Using Particle Swarm Optimisation

Plant Leaf Disease Detection and Classification Using Particle Swarm Optimization

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