A living environment prediction model using ensemble machine learning techniques based on quality of life index
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CitationErdoğan, Z., Namlı, E. (2019). A living environment rediction model using ensemble machine learning techniques based on quality of life index Journal of Ambient Intelligence and Humanized Computing, . Cited 3 times. https://doi.org/10.1007/s12652-019-01432-w
The living environment is an area that has the features necessary for all people to keep living their lives. Nevertheless, as time has progressed, needs and economic capacities of people have changed; consequently, the living environment has begun to express different meanings for different people. This differentiation brings the following question to mind: Can every person live in the same living environment under the same conditions? Of course not! People differ from each other in their preferences, needs, economic capacities, and many other aspects, so they must be assigned living environments that best fit their unique features. In this study, machine learning (ML) techniques and ensemble machine learning (EML) techniques are used for the establishment of a living environment prediction model. The data required for the quality of life index are obtained from a questionnaire. This questionnaire was prepared to consider people’s desires and economic capacities. Quality of life index is computed taking the weighted arithmetic mean of questionnaire data. Calculated quality of life indexes of the individuals is assigned to available quality of life classes of the cities. ML-based classification techniques have been used for prediction of assigned classes of the cities. A living environment prediction model is proposed in this study by using an ML and EML techniques-based quality of life index. For predicting living environment, ML-based methodologies include an artificial neural network (ANN), support vector machines (SVM) and EML-based methodologies include stacking and voting. The prediction results of ensemble models are better than prediction results of individual ML models, especially the stacking-based model composed of SMO + LMT and which reached the best performance values using the 80% split method. © 2019, Springer-Verlag GmbH Germany, part of Springer Nature.