Feature Extraction, Construction and Selection: A Data Mining Perspective (The Springer International Series in Engineering and Computer Science)

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There is a broad interest in feature extraction, construction, and selection among practitioners from statistics, pattern recognition, and data mining to machine learning. Data pre-processing is an essential step in the knowledge discovery process for real-world applications. This book compiles contributions from many leading and active researchers in this growing field and paints a picture of the state-of-the-art techniques that can boost the capabilities of many existing data mining tools.
The objective of this collection is to increase the awareness of the data mining community about research into feature extraction, construction and selection, which are currently conducted mainly in isolation. This book is part of an endeavor to produce a contemporary overview of modern solutions, to create synergy among these seemingly different branches, and to pave the way for developing meta-systems and novel approaches.
The book can be used by researchers and graduate students in machine learning, data mining, and knowledge discovery, who wish to understand techniques of feature extraction, construction and selection for data pre-processing and to solve large size, real-world problems. The book can also serve as a reference work for those who are conducting research into feature extraction, construction and selection, and are ready to meet the exciting challenges ahead of us.

Feature Extraction, Construction and Selection: A Data Mining Perspective (The Springer International Series in Engineering and Computer Science) Review

Wasn't what I was hoping for, which doesn't mean it's not what the authors intended.

I was looking for a book on identifying and extracting features, this is more what I would call "reducing features".

One of the hardest problems in dealing with data is IDENTIFYING the features used to describe the data set, and determining how to model them and use them, as well as figuring out if you need to use additional sources of data to make the features work.

This book is purely about mathematically producing new features from _already_ identified features. I.e. the hard part is done, now how can we make it more efficient/effective?

For what it is, it's the only book I see, so you might want to pick it up. But I suggest spending some time on 'search inside this book' to see if it meets your needs.

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