A weighted ensemble-based active learning model to label microarray data

De, Rajonya; Chakraborty, Anuran; Chatterjee, Agneet; Sarkar, Ram

doi:10.1007/s11517-020-02238-1

Cited by 4 publications

(2 citation statements)

References 33 publications

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“…The RF is an ensemble technique and it discovered by Breiman (2001). The RF technique merge several numbers of DTs into a tree and the final decision is based on the summation of all individual tree decision (De et al, 2020). Each tree is split by Gini index to identify the final target class for predicting the suitable fertilizer for a crop.…”

Section: Methodsmentioning

confidence: 99%

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Predicting the suitable fertilizer for crop based on soil and environmental factors using various feature selection techniques with classifiers

et al. 2022

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Agriculture is an essential part of human life. The crop productivity is based on the soil and environmental factors. Different crops are cultivated in different areas. Nowadays, the crop productivity level is affected by the climate change and diseases in the crops. Due to this pest infestation, the crop growth is heavily affected. To overcome this problem, the right fertilizer for a particular crop has to be chosen and fertilizer helps farmers to improve the crop productivity rate. This process can be done by using various machine learning techniques. In this work, various features selection techniques with classifiers used to predict the suitable fertilizer for a crop. The experimental results show that recursive feature elimination along the proposed Heterogeneous Stacked Ensemble classifier gives better prediction rate than other methods.

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Section: Methodsmentioning

confidence: 99%

“…The SVM (De et al, 2020) is a supervised techniques which decide the target class based on hyperplane. It also known as decision boundary.…”

Section: Support Vector Machinementioning

confidence: 99%

Predicting the suitable fertilizer for crop based on soil and environmental factors using various feature selection techniques with classifiers

et al. 2022

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Efficient Heart Disease Classification Through Stacked Ensemble with Optimized Firefly Feature Selection

Natarajan,

Vinoth Kumar,

Mahesh

et al. 2024

Int J Comput Intell Syst

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In the current century, heart-related sickness is one of the important causes of death for all humans. An estimated 17.5 million deaths occur due to heart disease worldwide. It is observed that more than 75% of peoples with average income level mostly suffer from heart diseases and its complications. So, there is need for predicting heart infection and its related complications. Data mining is the method of converting raw data into useful information. These tools allow given data to predict future trends. Data mining concepts were mainly adapted in heart disease data sets to interpret the intricate inferences out of it. In the modern world, many research are carried in health care engineering with the use of mining and prediction techniques. This investigation aims to identify significant features in heart disease dataset and to apply ensembling techniques for improving exactness of prediction. Prediction models are developed using different ensembling techniques like stacking and voting. For the experimental purpose, the Z-Alizadeh Sani dataset is used, which is available in the UCI machine learning data repository. Stacking and voting techniques are applied to the dataset. Stacking with substantial characteristics has the maximum accuracy of 86.79% in the Z-Alizadeh dataset. Test outcome proves that the prediction model implemented with the features selected using firefly algorithm and stacking-based classification model has the highest accuracy prediction than other technique. Furthermore, this study delineates a comparative analysis with prior works, showcasing the superior capabilities of the firefly algorithm in optimizing feature selection processes, which is crucial for advancing the accuracy of heart disease predictions.

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Gene reduction and machine learning algorithms for cancer classification based on microarray gene expression data: A comprehensive review

Osama

Shaban

Ali

2023

Expert Systems with Applications

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A weighted ensemble-based active learning model to label microarray data

Cited by 4 publications

References 33 publications

Predicting the suitable fertilizer for crop based on soil and environmental factors using various feature selection techniques with classifiers

Predicting the suitable fertilizer for crop based on soil and environmental factors using various feature selection techniques with classifiers

Efficient Heart Disease Classification Through Stacked Ensemble with Optimized Firefly Feature Selection

Gene reduction and machine learning algorithms for cancer classification based on microarray gene expression data: A comprehensive review

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