Distributed adaboost extensions for cost-sensitive classification problems
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Ahmedabad University
Abstract
In statistics and machine learning, the problem of identifying the categories to which a new sample belongs to, based on training data, is known as classification. It has always drawn the attention of researchers and more so after the rapid increase in the amount of data. Cost-sensitive classification is a subset of classification where the focus has always remained on solving the class imbalance problem. Boosting learning is one of the main methods of learning from such data. Over the past two decades, it has been applied to a variety of domains. Moreover, for applications in the real world, accuracy alone is not enough
there are costs involved- those when classification errors occur. Furthermore, the rapid increase in the amount of data has generated the data explosion problem. When data explosion, as well as class imbalance problems, occurs together, the existing cost-sensitive algorithms fail in terms of balancing between accuracy and cost measures. This dissertation attempts to address this very problem, using Cost-Sensitive Distributed Boosting (CsDb). CsDb is a meta-classifier designed to solve the class imbalance problem for big data. CsDb is an extension of distributed decision tree v.2 (DDTv2) and CSEx tensions. CsDb is based on the concept of MapReduce. The focus of the research is to solve the class imbalance problem for the size of data which is beyond the ca pacity of standalone commodity hardware to handle. CsDb solves the classification problem by a learning model in a distributed environment. The empirical evaluation carried over datasets from different application domains show reduced cost while preserving accuracy. This dissertation develops a new framework and an algorithm concentrating on the view that cost-sensitive boosting learning involves a trade-off between costs and accuracy. Decisions arising from these two viewpoints can often be incompatible, resulting in the reduction of the accuracy rates. Acomprehensive survey of cost-sensitive decision tree learning has identified over thirty algorithms, developing a taxonomy in order to classify the algorithms by the way in which cost has been incorporated, and a recent comparison shows that many cost-sensitive algorithms can process balanced, two-class datasets well, but produce lower accuracy rates in order to achieve lower costs when the dataset is imbalanced or has multiple classes. The new algorithm has been evaluated on eleven datasets and has been com pared with six algorithms CSE1-5 and DDTv2. The results obtained show that the new CsDb algorithm can produce more cost-effective trees without compromising on accuracy. The dissertation also includes a critical appraisal of the limitations of the algorithm developed and proposes avenues for further research.
there are costs involved- those when classification errors occur. Furthermore, the rapid increase in the amount of data has generated the data explosion problem. When data explosion, as well as class imbalance problems, occurs together, the existing cost-sensitive algorithms fail in terms of balancing between accuracy and cost measures. This dissertation attempts to address this very problem, using Cost-Sensitive Distributed Boosting (CsDb). CsDb is a meta-classifier designed to solve the class imbalance problem for big data. CsDb is an extension of distributed decision tree v.2 (DDTv2) and CSEx tensions. CsDb is based on the concept of MapReduce. The focus of the research is to solve the class imbalance problem for the size of data which is beyond the ca pacity of standalone commodity hardware to handle. CsDb solves the classification problem by a learning model in a distributed environment. The empirical evaluation carried over datasets from different application domains show reduced cost while preserving accuracy. This dissertation develops a new framework and an algorithm concentrating on the view that cost-sensitive boosting learning involves a trade-off between costs and accuracy. Decisions arising from these two viewpoints can often be incompatible, resulting in the reduction of the accuracy rates. Acomprehensive survey of cost-sensitive decision tree learning has identified over thirty algorithms, developing a taxonomy in order to classify the algorithms by the way in which cost has been incorporated, and a recent comparison shows that many cost-sensitive algorithms can process balanced, two-class datasets well, but produce lower accuracy rates in order to achieve lower costs when the dataset is imbalanced or has multiple classes. The new algorithm has been evaluated on eleven datasets and has been com pared with six algorithms CSE1-5 and DDTv2. The results obtained show that the new CsDb algorithm can produce more cost-effective trees without compromising on accuracy. The dissertation also includes a critical appraisal of the limitations of the algorithm developed and proposes avenues for further research.