An improved short term load forecasting with ranker based feature selection technique

Subbiah, Siva Sankari; Jayakumar, C.

doi:10.3233/jifs-191568

Cited by 18 publications

(9 citation statements)

References 36 publications

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“…GR and IG were adopted and applied to check whether they had a positive effect in increasing the performance accuracy of the supervised algorithms used in this paper. Indeed, and through the obtained results, it was proved that after their application, there was a relative increase in the performance of the algorithms [10].…”

Section: Ranker Feature Selectionmentioning

confidence: 76%

Comparative Study of Supervised Machine Learning Algorithms on Thoracic Surgery Patients based on Ranker Feature Algorithms

Abdulhadi¹,

Talabani²

2021

UHD J SCI TECH

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Thoracic surgery refers to the information gathered for the patients who have to suffer from lung cancer. Various machine learning techniques were employed in post-operative life expectancy to predict lung cancer patients. In this study, we have used the most famous and influential supervised machine learning algorithms, which are J48, Naïve Bayes, Multilayer Perceptron, and Random Forest (RF). Then, two ranker feature selections, information gain and gain ratio, were used on the thoracic surgery dataset to examine and explore the effect of used ranker feature selections on the machine learning classifiers. The dataset was collected from the Wroclaw University in UCI repository website. We have done two experiments to show the performances of the supervised classifiers on the dataset with and without employing the ranker feature selection. The obtained results with the ranker feature selections showed that J48, NB, and MLP’s accuracy improved, whereas RF accuracy decreased and support vector machine remained stable.

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Section: Ranker Feature Selectionmentioning

confidence: 76%

Comparative Study of Supervised Machine Learning Algorithms on Thoracic Surgery Patients based on Ranker Feature Algorithms

Abdulhadi¹,

Talabani²

2021

UHD J SCI TECH

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“…The filter feature selection selects the relevant features without considering any model. It considers only the statistical relationship between each feature and target feature [13]. Figure 2 shows the working of the filter feature selection.…”

Section: Filter Feature Selectionmentioning

confidence: 99%

“…The feature selection on big data can be easily performed using Spark framework. As illustrated in literature, the algorithms like MapReduce based evolutionary feature selection, evolutionary feature weighting, greedy information theoretic feature selection, fast-mRMR, prototype reduction algorithm, random oversampling and random forest are utilized for efficient feature selection in Spark environment [13].…”

Section: Feature Selection In Big Data Analysismentioning

confidence: 99%

Opportunities and Challenges of Feature Selection Methods for High Dimensional Data: A Review

Subbiah¹,

Jayakumar²

2021

ISI

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Now a day, all the organizations collecting huge volume of data without knowing its usefulness. The fast development of Internet helps the organizations to capture data in many different formats through Internet of Things (IoT), social media and from other disparate sources. The dimension of the dataset increases day by day at an extraordinary rate resulting in large scale dataset with high dimensionality. The present paper reviews the opportunities and challenges of feature selection for processing the high dimensional data with reduced complexity and improved accuracy. In the modern big data world the feature selection has a significance in reducing the dimensionality and overfitting of the learning process. Many feature selection methods have been proposed by researchers for obtaining more relevant features especially from the big datasets that helps to provide accurate learning results without degradation in performance. This paper discusses the importance of feature selection, basic feature selection approaches, centralized and distributed big data processing using Hadoop and Spark, challenges of feature selection and provides the summary of the related research work done by various researchers. As a result, the big data analysis with the feature selection improves the accuracy of the learning.

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“…The main objective of the big data analytics is to assist the predictive modelers, analytics professionals and data scientists in taking the right business decisions by analysing the large volume of transactional and other forms of data. It is utilized in various areas such as energy [50], finance [51], healthcare [52], text mining [53] and telecommunication [54], load forecasting [55]. Hence, the big data analytics adds much power to wind energy forecasting.…”

Section: Development Of Deep Learning Based Rnn In Wind Energy Forecastingmentioning

confidence: 99%

Deep Learning Based Recurrent Neural Networks to Enhance the Performance of Wind Energy Forecasting: A Review

Paramasivan¹

2021

RIA

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In the modern era, deep learning is a powerful technique in the field of wind energy forecasting. The deep neural network effectively handles the seasonal variation and uncertainty characteristics of wind speed by proper structural design, objective function optimization, and feature learning. The present paper focuses on the critical analysis of wind energy forecasting using deep learning based Recurrent neural networks (RNN) models. It explores RNN and its variants, such as simple RNN, Long Short Term Memory (LSTM), Gated Recurrent Unit (GRU), and Bidirectional RNN models. The recurrent neural network processes the input time series data sequentially and captures well the temporal dependencies exist in the successive input data. This review investigates the RNN models of wind energy forecasting, the data sources utilized, and the performance achieved in terms of the error measures. The overall review shows that the deep learning based RNN improves the performance of wind energy forecasting compared to the conventional techniques.

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An improved short term load forecasting with ranker based feature selection technique

Cited by 18 publications

References 36 publications

Comparative Study of Supervised Machine Learning Algorithms on Thoracic Surgery Patients based on Ranker Feature Algorithms

Comparative Study of Supervised Machine Learning Algorithms on Thoracic Surgery Patients based on Ranker Feature Algorithms

Opportunities and Challenges of Feature Selection Methods for High Dimensional Data: A Review

Deep Learning Based Recurrent Neural Networks to Enhance the Performance of Wind Energy Forecasting: A Review

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