We are living in a big data world where enormous data as a flood is brimming from all around to spawn Data Ocean. These data are fascinating if handled appropriately or else it is nothing more than trash. An ordinary algorithm is not competent in dealing out this mammoth dataset, as they are programmed to work based on the instruction. At present machine learning and data mining is gaining esteem as it is consists of a wide range of robust algorithms, which is capable of dispensation big data. The main aspiration of this work is to recognize the performances hurdle of machine learning classification algorithm due to complexity added by imbalance dataset for training purpose. The main contribution of this work is to generate a hybridization pre-processing and resampling technique which will able to reduce the complexity due to an imbalance big datasets and thus enhances performances of ML classification algorithms during assembling a precise predictive model. The algorithm proposed in this book, Hybridization Preprocessing and Resampling Technique (HPRT) is an enhanced technique, designed to reduce the complexity of dataset.
We are living in a big data world where enormous data as a flood is brimming from all around to spawn Data Ocean. These data are fascinating if handled appropriately or else it is nothing more than trash. An ordinary algorithm is not competent in dealing out this mammoth dataset, as they are programmed to work based on the instruction. At present machine learning and data mining is gaining esteem as it is consists of a wide range of robust algorithms, which is capable of dispensation big data. The main aspiration of this work is to recognize the performances hurdle of machine learning classification algorithm due to complexity added by imbalance dataset for training purpose. The main contribution of this work is to generate a hybridization pre-processing and resampling technique which will able to reduce the complexity due to an imbalance big datasets and thus enhances performances of ML classification algorithms during assembling a precise predictive model. The algorithm proposed in this book, Hybridization Preprocessing and Resampling Technique (HPRT) is an enhanced technique, designed to reduce the complexity of dataset.
Build strong foundation for entering the world of Machine Learning and data science with the help of this comprehensive guideKey Features:• Get started in the field of Machine Learning with the help...
Publisher's Note: This edition from 2018 is outdated and is not compatible with TensorFlow 2 or any of the most recent updates to Python libraries. A new second edition, updated for 2020 with...
This book constitutes the refereed proceedings of the Second Symposium on Machine Learning and Metaheuristics Algorithms, and Applications, SoMMA 2020, held in Chennai, India, in October...
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