The objective of this study is to develop an ensemble classifier with Merit Merge feature selection that will enhance efficiency of classification in a multivariate multiclass medical data for effective disease diagnostics. The large volumes of features extracted from brain Magnetic Resonance Images and neuropsychological tests for diagnosis lead to more complexity in classification procedures. A higher level of objectivity than what readers have is needed to produce reliable dementia diagnostic techniques. Ensemble approach which is trained with features selected from multiple biomarkers facilitated accurate classification when compared with conventional classification techniques. Ensemble approach for feature selection is experimented with classifiers like Naïve Bayes, Random forest, Support Vector Machine, and C4.5. Feature search is done with Particle Swarm Optimisation to retrieve the subset of features for further selection with the ensemble classifier. Features selected by the proposed C4.5 ensemble classifier with Particle Swarm Optimisation search, coupled with Merit Merge technique (CPEMM), outperformed bagging feature selection of SVM, NB, and Random forest classifiers. The proposed CPEMM feature selection found the best subset of features that efficiently discriminated normal individuals and patients affected with Mild Cognitive Impairment and Alzheimer's Dementia with 98.7% accuracy.
Background: Nowadays, the mobile app market becomes rapidly increased in world wide. The mobile app marketers have smart enough to understand the requirements and demands of customers and perform their aspirations. They delight them. It provides growth, profitability, and creativity with lot of inventions. The main aim of this research is to analyze the customer interest and preferences of mobile service providers. Methodology: This paper proposed the clustering model named as Hierarchical Flexi-Ensemble Clustering (HFEC). It provides the final result with robustness and improved quality. Before clustering, the unwanted features are removed by using the Genetic Algorithm based on the Collective Materials (GACM) technique. The customer preferences are analyzes with the clustering of mobile usage patterns. Results: The analysis determined that the app usage pattern based on the most frequent word, rating category, rating character count, rating word count and content-based rating in the google play store app dataset. Finally, the results are compared with the existing methods to analyze the superior performance of proposed method. The comparison analysis is estimated based on the based on the average hit rate at different cache sizes.
Conclusion:The work is concluded with the app pattern prediction in the form of clustering for app marketing service. From the marketing side, they can analyze the customer preferences and satisfaction.
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