Voice Feature Selection to Improve Performance of Machine Learning Models for Voice Production Inversion

2021 ◽  
Author(s):  
Zhaoyan Zhang
Keyword(s):  
Machine Learning ◽  
Feature Selection ◽  
Learning Models ◽  
Improve Performance ◽  
Voice Production ◽  
Machine Learning Models

scholarly journals A Novel Method for Performance Measurement of Public Educational Institutions Using Machine Learning Models

2021 ◽  
Vol 11 (19) ◽  
pp. 9296
Author(s):  
Talha Mahboob Alam ◽  
Mubbashar Mushtaq ◽  
Kamran Shaukat ◽  
Ibrahim A. Hameed ◽  
Muhammad Umer Sarwar ◽  
...  
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Feature Selection ◽  
Artificial Neural Networks ◽  
Performance Indicators ◽  
Key Performance Indicators ◽  
Educational Institutions ◽  
Institutional Performance ◽  
Learning Models ◽  
Machine Learning Models

Lack of education is a major concern in underdeveloped countries because it leads to poor human and economic development. The level of education in public institutions varies across all regions around the globe. Current disparities in access to education worldwide are mostly due to systemic regional differences and the distribution of resources. Previous research focused on evaluating students’ academic performance, but less has been done to measure the performance of educational institutions. Key performance indicators for the evaluation of institutional performance differ from student performance indicators. There is a dire need to evaluate educational institutions’ performance based on their disparities and academic results on a large scale. This study proposes a model to measure institutional performance based on key performance indicators through data mining techniques. Various feature selection methods were used to extract the key performance indicators. Several machine learning models, namely, J48 decision tree, support vector machines, random forest, rotation forest, and artificial neural networks were employed to build an efficient model. The results of the study were based on different factors, i.e., the number of schools in a specific region, teachers, school locations, enrolment, and availability of necessary facilities that contribute to school performance. It was also observed that urban regions performed well compared to rural regions due to the improved availability of educational facilities and resources. The results showed that artificial neural networks outperformed other models and achieved an accuracy of 82.9% when the relief-F based feature selection method was used. This study will help support efforts in governance for performance monitoring, policy formulation, target-setting, evaluation, and reform to address the issues and challenges in education worldwide.

Neural Networks in ECG Classification

2011 ◽  
pp. 81-104 ◽  
Author(s):  
G. Camps-Valls ◽  
J. F. Guerrero-Martinez
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Feature Selection ◽  
Adaptive Systems ◽  
Clinical Environment ◽  
Learning Models ◽  
Cardiac Pathology ◽  
Learning Tasks ◽  
Beat Detection ◽  
Machine Learning Models

In this chapter, we review the vast field of application of artificial neural networks in cardiac pathology discrimination based on electrocardiographic signals. We discuss advantages and drawbacks of neural and adaptive systems in cardiovascular medicine and catch a glimpse of forthcoming developments in machine learning models for the real clinical environment. Some problems are identified in the learning tasks of beat detection, feature selection/extraction, and classification, and some proposals and suggestions are given to alleviate the problems of interpretability, overfitting, and adaptation. These have become important problems in recent years and will surely constitute the basis of some investigations in the immediate future.

Importance of Feature Selection and Data Visualization Towards Prediction of Breast Cancer

2019 ◽  
Vol 12 (4) ◽  
pp. 317-328 ◽  
Author(s):  
Rajalakshmi Krishnamurthi ◽  
Niyati Aggrawal ◽  
Lokendra Sharma ◽  
Diva Srivastava ◽  
Shivangi Sharma
Keyword(s):  
Breast Cancer ◽  
Machine Learning ◽  
Feature Selection ◽  
Support Vector ◽  
Data Visualisation ◽  
Learning Models ◽  
Cancer Data ◽  
Comparison Results ◽  
Tree Classifier ◽  
Machine Learning Models

Background: Breast cancer is one of the most common forms of cancers among women and the leading cause of death among them. Countries like United States, England and Canada have reported a high number of breast cancer patients every year and this number is continuously increasing due to detection at later stages. Hence, it is very important to create awareness among women and develop such algorithms which help to detect malignant cancer. Several research studies have been conducted to analyze the breast cancer data. Objective: This paper presents an effective method in predicting breast cancer and its stage and will also analyze the performance of different supervised learning algorithms such as Random Classifier, Chi2 Square test used in order to predict. The paper focuses on the three important aspects such as the feature selection, the corresponding data visualisation and finally making a prediction call on different machine learning models. Methods: The dataset used for this work is breast cancer Wisconsin data taken from UCI library. The dataset has been used to show the different 32 features which are all important and how it can be achieved using data visualisation. Secondly, after the feature selection, different machine learning models have been applied. Conclusion: The machine learning models involved are namely Support Vector Machine (SVM), KNearest Neighbour (KNN), Random Forest, Principal Component Analysis (PCA), Neural Network using Perceptron (NNP). This has been done to check which type of model is better under what conditions. At different stages several charts have been plotted and eliminated based on relative comparison. Results have shown that Random Tree classifier along with Chi2 Square proves to be an efficient one.

scholarly journals Feature Selection to Win the Point of ATP Tennis Players Using Rally Information

2020 ◽  
Vol 19 (1) ◽  
pp. 37-50
Author(s):  
M. Makino ◽  
T. Odaka ◽  
J. Kuroiwa ◽  
I. Suwa ◽  
H. Shirai
Keyword(s):  
Machine Learning ◽  
Logistic Regression ◽  
Feature Selection ◽  
Area Under The Curve ◽  
Prominent Feature ◽  
Learning Models ◽  
Tennis Players ◽  
Machine Learning Models ◽  
Tennis Match

AbstractIn tennis, the accumulation of data has progressed and research on tactical analysis has been conducted. Estimating strategically important factors would have the benefit of providing players with useful advice and helping audience members understand what tennis players are good at. Previous research has been conducted into ways of predicting Association of Tennis Professionals (ATP) tennis match outcomes as well as estimating factors that are important for victories using machine learning models. The challenge of previous research is that the victory factor lacks concreteness. Since we thought the root of the abovementioned problem was that previous researchers used game summary as a feature and did not consider the process of rallies between points, this research focused on calculating the frequency of single shots, two-shot patterns, and specific effective shot patterns from each point rally of ATP singles matches. We then used those data to predict point winners and useful features using L1-regularized logistic regression. The highest accuracy obtained was 66.5%, and the area under the curve (AUC) was 0.689. The most prominent feature we found was the ratio of specific shots by specific players. From these results, our method could reveal more concretely tactical factors than previous studies.

scholarly journals Integrating Feature Selection and Multiclass Classification for Sport Result Prediction

2019 ◽  
Vol 9 (1) ◽  
pp. 1339-1342
Keyword(s):  
Machine Learning ◽  
Feature Selection ◽  
Learning Algorithms ◽  
Multiclass Classification ◽  
Machine Learning Algorithms ◽  
Classification Algorithms ◽  
Learning Models ◽  
Sports Fans ◽  
Player Performance ◽  
Machine Learning Models

Machine learning (ML) has become the most predominant methodology that shows good results in the classification and prediction domains. Predictive systems are being employed to predict events and its results in almost every walk of life. The field of prediction in sports is gaining importance as there is a huge community of betters and sports fans. Moreover team owners and club managers are struggling for Machine learning models that could be used for formulating strategies to win matches. Numerous factors such as results of previous matches, indicators of player performance and opponent information are required to build these models. This paper provides an analysis of such key models focusing on application of machine learning algorithms to sport result prediction. The results obtained helped us to elucidate the best combination of feature selection and classification algorithms that render maximum accuracy in sport result prediction.

scholarly journals Return of the features

2018 ◽  
Vol 616 ◽  
pp. A97 ◽  
Author(s):  
A. D’Isanto ◽  
S. Cavuoti ◽  
F. Gieseke ◽  
K. L. Polsterer
Keyword(s):  
Machine Learning ◽  
Feature Selection ◽  
Selection Process ◽  
Sloan Digital Sky Survey ◽  
Learning Models ◽  
Classification Problems ◽  
Forward Selection ◽  
Feature Sets ◽  
Physical Context ◽  
Machine Learning Models

Context. The explosion of data in recent years has generated an increasing need for new analysis techniques in order to extract knowledge from massive data-sets. Machine learning has proved particularly useful to perform this task. Fully automatized methods (e.g. deep neural networks) have recently gathered great popularity, even though those methods often lack physical interpretability. In contrast, feature based approaches can provide both well-performing models and understandable causalities with respect to the correlations found between features and physical processes. Aims. Efficient feature selection is an essential tool to boost the performance of machine learning models. In this work, we propose a forward selection method in order to compute, evaluate, and characterize better performing features for regression and classification problems. Given the importance of photometric redshift estimation, we adopt it as our case study. Methods. We synthetically created 4520 features by combining magnitudes, errors, radii, and ellipticities of quasars, taken from the Sloan Digital Sky Survey (SDSS). We apply a forward selection process, a recursive method in which a huge number of feature sets is tested through a k-Nearest-Neighbours algorithm, leading to a tree of feature sets. The branches of the feature tree are then used to perform experiments with the random forest, in order to validate the best set with an alternative model. Results. We demonstrate that the sets of features determined with our approach improve the performances of the regression models significantly when compared to the performance of the classic features from the literature. The found features are unexpected and surprising, being very different from the classic features. Therefore, a method to interpret some of the found features in a physical context is presented. Conclusions. The feature selection methodology described here is very general and can be used to improve the performance of machine learning models for any regression or classification task.

Sign in / Sign up

Export Citation Format

Share Document