Research Paper Classification using Supervised Machine Learning Techniques

Chowdhury, Shovan; Schoen, Marco P.

doi:10.1109/ietc47856.2020.9249211

Cited by 51 publications

(15 citation statements)

References 3 publications

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“…To determine the cluster of a future date, a classification algorithm LightGBM is employed. Classification models a predictive problem where a label is predicted by input data [47]. The classification algorithm takes in a multivariate dataset and a label as a requirement.…”

Section: G Forecast Model Selectionmentioning

confidence: 99%

Spatiotemporal Sequence-to-Sequence Clustering for Electric Load Forecasting

Acquah

Jin

et al. 2023

IEEE Access

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Massive electrical load exhibits many patterns making it difficult for forecast algorithms to generalise well. Most learning algorithms produce a better forecast for dominant patterns in the case of weekday consumption and otherwise for less dominant patterns in weekend and holiday consumption. In view of this, there is the need to cluster the load patterns, so learning algorithms can focus on the patterns independently to produce forecasts with better accuracy for all cases. However, clustering time-series data breaks the time-series dependency, making model training difficult. This paper presents a novel sequence-to-sequence cluster framework to reform time-series dependency after clustering; this enables independent clusters to be modelled using Convolutional Neural Network-Gated Recurrent Unit, which learns spatiotemporal features for future forecasts. A real-world dataset by the Korea Power Exchange composed of nationwide consumption is used for case studies and experiments. Experimental results verify that the proposed study effectively improves the accuracy of electric load forecasting by about 50%, with a WAPE of 0.67%. The proposed method also speeds up the training process of the forecast algorithm by about 35%, given that only a subset of the dataset is trained due to clustering. Korea Water Resources Corporation has implemented the proposed method for load forecasting and system marginal price estimation.INDEX TERMS Convolutional neural network-gated recurrent unit (CNN-GRU), feature engineering, kmeans clustering, LightGBM classifier, sequence-to-sequence forecast, short-term load forecast (STLF).

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Section: G Forecast Model Selectionmentioning

confidence: 99%

Spatiotemporal Sequence-to-Sequence Clustering for Electric Load Forecasting

Acquah

Jin

et al. 2023

IEEE Access

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“…The Decision tree algorithm is a non-parametric supervised machine learning method that is used for both classification and regression. It is a tree like structure consists of several branch (see , where each internal node denotes a test on an attribute, each branch represents an outcome of the test, and each leaf node (terminal node) holds a predicted value [18]. Among four types of decision tree algorithm: Iterative Dichotomiser (ID3), Classification and Regression Trees (CART), Chi-Square and Reduction in Variance, the CART algorithm is being widely used for regression problems.…”

Section: Decision Treementioning

confidence: 99%

Evaluation of Tree Based Regression over Multiple Linear Regression for Non-normally Distributed Data in Battery Performance

Chowdhury¹,

Lin²,

Liaw³

et al. 2021

Preprint

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Battery performance datasets are typically non-normal and multicollinear. Extrapolating such datasets for model predictions needs attention to such characteristics. This study explores the impact of data normality in building machine learning models. In this work, tree-based regression models and multiple linear regressions models are each built from a highly skewed non-normal dataset with multicollinearity and compared. Several techniques are necessary, such as data transformation, to achieve a good multiple linear regression model with this dataset; the most useful techniques are discussed. With these techniques, the best multiple linear regression model achieved an R 2 = 81.23% and exhibited no multicollinearity effect for the dataset used in this study. Tree-based models perform better on this dataset, as they are non-parametric, capable of handling complex relationships among variables and not affected by multicollinearity. We show that bagging, in the use of Random Forests, reduces overfitting. Our best tree-based model achieved accuracy of R 2 = 97.73%. This study explains why tree-based regressions promise as a machine learning model for non-normally distributed, multicollinear data.

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“…Machine learning (ML) approaches are of high importance in disease prediction in this regard. The best method for the labelled data is classification, which is a supervised ML method [4], [5]. Therefore, depending on the test results of patients, the classification approaches might be used for predicting the disease.…”

Section: Introductionmentioning

confidence: 99%

Effectiveness evaluation of machine learning algorithms for breast cancer prediction

et al. 2022

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Breast cancer is becoming a global epidemic, affecting predominantly women. As a result, the number of people diagnosed with breast cancer is increasing every day. As a result, it is critical to have certain early detection methods in place that can assist patients in recognizing this condition at an early stage. Therefore, they might begin taking their medication to prevent the sickness from killing them. Different prediction approaches for early diagnosis of such diseases have been created in the machine learning fields. Those algorithms employ a variety of computational classifiers and claim to achieve satisfactory results in a few areas. However, no research was reached to determine which computationally sophisticated approach is more effective in detecting breast cancer. As a result, it is necessary to select the most effective strategy from the available options. This paper makes a contribution to the performance evaluation of 12 alternative classification strategies on datasets of breast cancer. The right explanations for the classifiers' dominance were investigated.

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Research Paper Classification using Supervised Machine Learning Techniques

Cited by 51 publications

References 3 publications

Spatiotemporal Sequence-to-Sequence Clustering for Electric Load Forecasting

Spatiotemporal Sequence-to-Sequence Clustering for Electric Load Forecasting

Evaluation of Tree Based Regression over Multiple Linear Regression for Non-normally Distributed Data in Battery Performance

Effectiveness evaluation of machine learning algorithms for breast cancer prediction

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