Using stacking ensemble for microarray-based cancer classification

For many years there has been a focus on individual welfare and societal advancement. In addition to the economic system, diverse experiences and the habitats of people are crucial factors that contribute to the well-being and progress of the nation. The predictor of quality of life called the Better Life Index (BLI) visualizes and compares key elements—environment, jobs, health, civic engagement, governance, education, access to services, housing, community, and income—that contribute to well-being in different countries. This paper presents a supervised machine-learning analytical model that predicts the life satisfaction score of any specific country based on these given parameters. This work is a stacked generalization based on a novel approach that combines different machine-learning approaches to generate a meta-machine-learning model that further aids in maximizing prediction accuracy. The work utilized an Organization for Economic Cooperation and Development (OECD) regional statistics dataset with four years of data, from 2014 to 2017. The novel model achieved a high root mean squared error (RMSE) value of 0.3 with 10-fold cross-validation on the balanced class data. Compared to base models, the ensemble model based on the stacked generalization framework was a significantly better predictor of the life satisfaction of a nation. It is clear from the results that the ensemble model presents more precise and consistent predictions in comparison to the base learners.

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“…The final output was selected using the stacking technique [26]. The flow of the proposed methodology is presented in Figure 3.…”

Section: Proposed Methodologymentioning

confidence: 99%

Supervised Machine-Learning Predictive Analytics for National Quality of Life Scoring

et al. 2019

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“…Ensemble learning technique describes a process in which multiple classifiers are trained to generate decision insights based on different classifiers through random subspace, bagging, and boosting approaches for increased performance [17]. The most common ensemble learning approaches include weighted averages, majority voting, and simple averages.…”

Section: Classification Techniquesmentioning

confidence: 99%

“…The most common ensemble learning approaches include weighted averages, majority voting, and simple averages. Ensemble techniques combines multiple classifiers in determining the optimal classification model from different sub-models comprising of a base classifier layer and metaclassifier layers, which make accurate predictions [17].…”

Section: Classification Techniquesmentioning

confidence: 99%

Investigate the Ensemble Model by Intelligence Analysis to Improve the Accuracy of the Classification Data in the Diagnostic and Treatment Interventions for Prostate Cancer

Karrar¹

2022

IJACSA

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Class imbalance problem become greatest issue in data mining, imbalanced data appears in daily application, especially in the health care. This research aims at investigating the application of ensemble model by intelligence analysis to improving the classification accuracy of imbalanced data sets on prostate cancer. The primary requirements obtained for this study included the datasets, relevant tools for pre-processing to identify the missing values, models for attribute selection and cross validation, data resembling framework, and intelligent algorithms for base classification. Additionally, the ensemble model and meta-learning algorithms were acquired in preparation for performance evaluation by embedding feature selecting capabilities into the classification model. The experimental results led to the conclusion that the application of ensemble learning algorithm on resampled data sets provides highly accurate classification results on single classifier J48. The study further suggests that gain ratio and ranker techniques are highly effective for attribute selection in the analysis of prostate cancer data. The lowest error rate and optimal performance accuracy in the classification of imbalanced prostate cancer data is achieved using when Adaboost algorithm is combined with single classifier J48.

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“…The stacking ensemble is an ensemble learning method that combines different types of base learning models to create a new model. Stacking ensemble learning builds up metadata by stacking the predicted values of the base models and derives the final predictive values using meta learners [50,51].…”

Section: Stacking Ensemble With Cross-validationmentioning

confidence: 99%

A Deep-Learning Approach for Foot-Type Classification Using Heterogeneous Pressure Data

Chae

Kang

Noh

2020

Sensors

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The human foot is easily deformed owing to the innate form of the foot or an incorrect walking posture. Foot deformations not only pose a threat to foot health but also cause fatigue and pain when walking; therefore, accurate diagnoses of foot deformations are required. However, the measurement of foot deformities requires specialized personnel, and the objectivity of the diagnosis may be insufficient for professional medical personnel to assess foot deformations. Thus, it is necessary to develop an objective foot deformation classification model. In this study, a model for classifying foot types is developed using image and numerical foot pressure data. Such heterogeneous data are used to generate a fine-tuned visual geometry group-16 (VGG16) and K−nearest neighbor (k-NN) models, respectively, and a stacking ensemble model is finally generated to improve accuracy and robustness by combining the two models. Through k-fold cross-validation, the accuracy and robustness of the proposed method have been verified by the mean and standard deviation of the f1 scores (0.9255 and 0.0042), which has superior performance compared to single models generated using only numerical or image data. Thus, the proposed model provides the objectivity of diagnosis for foot deformation, and can be used for analysis and design of foot healthcare products.

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Using stacking ensemble for microarray-based cancer classification

Cited by 24 publications

References 30 publications

Supervised Machine-Learning Predictive Analytics for National Quality of Life Scoring

Supervised Machine-Learning Predictive Analytics for National Quality of Life Scoring

Investigate the Ensemble Model by Intelligence Analysis to Improve the Accuracy of the Classification Data in the Diagnostic and Treatment Interventions for Prostate Cancer

A Deep-Learning Approach for Foot-Type Classification Using Heterogeneous Pressure Data

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