scholarly journals Multi-Objective Heuristic Feature Selection for Speech-Based Multilingual Emotion Recognition

2016 ◽  
Vol 6 (4) ◽  
pp. 243-253 ◽  
Author(s):  
Christina Brester ◽  
Eugene Semenkin ◽  
Maxim Sidorov
Keyword(s):  
Genetic Algorithms ◽  
Feature Selection ◽  
Emotion Recognition ◽  
Optimization Model ◽  
Heuristic Procedure ◽  
Selection Methods ◽  
Feature Selection Technique ◽  
Selection Technique ◽  
Selection For ◽  
High Level

Abstract If conventional feature selection methods do not show sufficient effectiveness, alternative algorithmic schemes might be used. In this paper we propose an evolutionary feature selection technique based on the two-criterion optimization model. To diminish the drawbacks of genetic algorithms, which are applied as optimizers, we design a parallel multicriteria heuristic procedure based on an island model. The performance of the proposed approach was investigated on the Speech-based Emotion Recognition Problem, which reflects one of the most essential points in the sphere of human-machine communications. A number of multilingual corpora (German, English and Japanese) were involved in the experiments. According to the results obtained, a high level of emotion recognition was achieved (up to a 12.97% relative improvement compared with the best F-score value on the full set of attributes).

scholarly journals A Novel Feature Selection Technique to Better Predict Climate Change Stage of Change

2021 ◽  
Vol 14 (1) ◽  
pp. 40
Author(s):  
Hamed Naseri ◽  
E. Owen D. Waygood ◽  
Bobin Wang ◽  
Zachary Patterson ◽  
Ricardo A. Daziano
Keyword(s):  
Climate Change ◽  
Feature Selection ◽  
Environmental Concern ◽  
Stage Of Change ◽  
Principal Component ◽  
Selection Methods ◽  
Feature Selection Technique ◽  
Selection Technique ◽  
Testing Data ◽  
New Feature

Indications of people’s environmental concern are linked to transport decisions and can provide great support for policymaking on climate change. This study aims to better predict individual climate change stage of change (CC-SoC) based on different features of transport-related behavior, General Ecological Behavior, New Environmental Paradigm, and socio-demographic characteristics. Together these sources result in over 100 possible features that indicate someone’s level of environmental concern. Such a large number of features may create several analytical problems, such as overfitting, accuracy reduction, and high computational costs. To this end, a new feature selection technique, named the Coyote Optimization Algorithm-Quadratic Discriminant Analysis (COA-QDA), is first proposed to find the optimal features to predict CC-SoC with the highest accuracy. Different conventional feature selection methods (Lasso, Elastic Net, Random Forest Feature Selection, Extra Trees, and Principal Component Analysis Feature Selection) are employed to compare with the COA-QDA. Afterward, eight classification techniques are applied to solve the prediction problem. Finally, a sensitivity analysis is performed to determine the most important features affecting the prediction of CC-SoC. The results indicate that COA-QDA outperforms conventional feature selection methods by increasing average testing data accuracy from 0.7 to 5.6%. Logistic Regression surpasses other classifiers with the highest prediction accuracy.

A novel supervised feature selection technique based on genetic algorithms

2012 ◽  
Author(s):  
Mattia Pedergnana ◽  
Prashanth Reddy Marpu ◽  
Mauro Dalla Mura ◽  
Jon Atli Benediktsson ◽  
Lorenzo Bruzzone
Keyword(s):  
Genetic Algorithms ◽  
Feature Selection ◽  
Feature Selection Technique ◽  
Selection Technique

Identification of Secretory Proteins of Malaria Parasite by Feature Selection Technique

2017 ◽  
Vol 14 (9) ◽  
Author(s):  
Hua Tang ◽  
Chunmei Zhang ◽  
Rong Chen ◽  
Po Huang ◽  
Chenggang Duan ◽  
...  
Keyword(s):  
Feature Selection ◽  
Malaria Parasite ◽  
Secretory Proteins ◽  
Feature Selection Technique ◽  
Selection Technique

An intelligent approach towards very short-term load forecasting

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Uttamarani Pati ◽  
Papia Ray ◽  
Arvind R. Singh
Keyword(s):  
Genetic Algorithm ◽  
Feature Selection ◽  
Load Forecasting ◽  
Dew Point ◽  
Short Term ◽  
Spinning Reserve ◽  
Feature Selection Technique ◽  
Selection Technique ◽  
Load Demand ◽  
Short Term Load Forecasting

Abstract Very short term load forecasting (VSTLF) plays a pivotal role in helping the utility workers make proper decisions regarding generation scheduling, size of spinning reserve, and maintaining equilibrium between the power generated by the utility to fulfil the load demand. However, the development of an effective VSTLF model is challenging in gathering noisy real-time data and complicates features found in load demand variations from time to time. A hybrid approach for VSTLF using an incomplete fuzzy decision system (IFDS) combined with a genetic algorithm (GA) based feature selection technique for load forecasting in an hour ahead format is proposed in this research work. This proposed work aims to determine the load features and eliminate redundant features to form a less complex forecasting model. The proposed method considers the time of the day, temperature, humidity, and dew point as inputs and generates output as forecasted load. The input data and historical load data are collected from the Northern Regional Load Dispatch Centre (NRLDC) New Delhi for December 2009, January 2010 and February 2010. For validation of proposed method efficacy, it’s performance is further compared with other conventional AI techniques like ANN and ANFIS, which are integrated with genetic algorithm-based feature selection technique to boost their performance. These techniques’ accuracy is tested through their mean absolute percentage error (MAPE) and normalized root mean square error (nRMSE) value. Compared to other conventional AI techniques and other methods provided through previous studies, the proposed method is found to have acceptable accuracy for 1 h ahead of electrical load forecasting.

Sign in / Sign up

Export Citation Format

Share Document