Abstract
With Internet of Things (IoT) being prevalently adopted in recent years, traditional machine learning and data mining methods can hardly be competent to deal with the complex big data problems if applied alone. However, hybridizing those who have complementary advantages could achieve optimized practical solutions. This work discusses how to solve multivariate regression problems and extract intrinsic knowledge by hybridizing Self-Organizing Maps (SOM) and Regression Trees. A dual-layer SOM map is developed in which the first layer accomplishes unsupervised learning and then regression tree layer performs supervised learning in the second layer to get predictions and extract knowledge. In this framework, SOM neurons serve as kernels with similar training samples mapped so that regression tree could achieve regression locally. In this way, the difficulties of applying and visualizing local regression on high dimensional data are overcome. Further, we provide an automated growing mechanism based on a few stop criteria without adding new parameters. A case study of solving Electrical Vehicle (EV) range anxiety problem is presented and it demonstrates that our proposed hybrid model is quantitatively precise and interpretive. key words: Multivariate Regression, Big Data, Machine Learning, Data Mining, Self-Organizing Maps (SOM), Regression Tree, Electrical Vehicle (EV), Range Estimation, Internet of Things (IoT)
Highlights
The central problems of Artificial Intelligence (AI) research include knowledge representation, reasoning, planning, learning, perception and the ability to control [1]
When the hidden layer of the Radial basis function (RBF) network is specified in the manner described in (2.17), we find that any correlation existing between adjacent data points in the training sample is correspondingly transplanted into adjacent units in the hidden layer
We aim to demonstrate that our proposed hybrid model could resolve the complex power consumption estimation problem practically
Summary
The central problems (or goals) of Artificial Intelligence (AI) research include knowledge representation, reasoning, planning, learning, perception and the ability to control [1]. Knowledge representation and reasoning (KRR) are central to AI research. Rather than general problem solvers, by the end of last century, AI changed its focus to logical, knowledge-based approach ,i.e. expert systems, that could match human competence on a specific task, which is still popular in practice. Expert systems gave us the terminology still in use today where AI systems are divided into a Knowledge Base (KB) with facts about the world and rules and an inference engine that applies the rules to the knowledge base in order to answer questions and solve problems. In most expert systems, just as the name indicated, the knowledge base and inference rules are usually provided by domain experts. The knowledge base tended to be a fairly flat structure, essentially assertions about the values of variables used by the rules
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