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What is Information Gain

Chapter 7
Handbook of Research on Advancing Cybersecurity for Digital Transformation
Information gain is the reduction in entropy or surprise by transforming a dataset and is calculated by comparing the entropy of the dataset before and after a transformation.
Published in Chapter:
Advancing Artificial Intelligence-Enabled Cybersecurity for the Internet of Things
Alper Kamil Demir (Adana Alparsalan Turkes Science and Technology University, Turkey) and Shahid Alam (Adana Alparsalan Turkes Science and Technology University, Turkey)
Copyright: © 2021 |Pages: 26
DOI: 10.4018/978-1-7998-6975-7.ch007
Abstract
Internet of things (IoT) has revolutionized digital transformation and is present in every sector including transportation, energy, retail, healthcare, agriculture, etc. While stepping into the new digital transformation, these sectors must contemplate the risks involved. The new wave of cyberattacks against IoT is posing a severe impediment in adopting this leading-edge technology. Artificial intelligence (AI) is playing a key role in preventing and mitigating some of the effects of these cyberattacks. This chapter discusses different types of threats and attacks against IoT devices and how AI is enabling the detection and prevention of these cyberattacks. It also presents some challenges faced by AI-enabled detection and prevention and provides some solutions and recommendations to these challenges. The authors believe that this chapter provides a favorable basis for the readers who intend to know more about AI-enabled technologies to detect and prevent cyberattacks against IoT and the motivation to advance the current research in this area.
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Chapter 16
Machine Learning Approach to Search Query Classification
The amount of information in a given set of data can be defined as (1 – entropy). If any observation about the given data is made, new information can then be recomputed. The difference between the two information values is the “information gain”. In other words, the change of entropy is the information that is gained by the observation. If we partition a set T into T1 and T0, based upon some characteristic of the data then the information gain of that partition can be defined as .
Published in Chapter: Machine Learning Approach to Search Query Classification; From: Handbook of Research on Web Log Analysis
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Chapter 171
Expert Knowledge in Data Mining
The change in information entropy from the current state of the set of instances to the proposed state of the set of instances. The entropy, H(s) , is a measure of the randomness of the distribution of the instances in a subset (s) of instances with respect to the dependent variable, d . where H(s) is the entropy of a set, s , and P(v i ) is the probability that v i is a value of attribute i .
Published in Chapter: Expert Knowledge in Data Mining; From: Encyclopedia of Information Science and Technology, Third Edition
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