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Books > Computing & IT > Applications of computing > Artificial intelligence > Knowledge-based systems / expert systems
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Modern Data Mining Algorithms in C++ and CUDA C - Recent Developments in Feature Extraction and Selection Algorithms for Data Science (Paperback, 1st ed.)
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Modern Data Mining Algorithms in C++ and CUDA C - Recent Developments in Feature Extraction and Selection Algorithms for Data Science (Paperback, 1st ed.)
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Discover a variety of data-mining algorithms that are useful for
selecting small sets of important features from among unwieldy
masses of candidates, or extracting useful features from measured
variables. As a serious data miner you will often be faced with
thousands of candidate features for your prediction or
classification application, with most of the features being of
little or no value. You'll know that many of these features may be
useful only in combination with certain other features while being
practically worthless alone or in combination with most others.
Some features may have enormous predictive power, but only within a
small, specialized area of the feature space. The problems that
plague modern data miners are endless. This book helps you solve
this problem by presenting modern feature selection techniques and
the code to implement them. Some of these techniques are: Forward
selection component analysis Local feature selection Linking
features and a target with a hidden Markov model Improvements on
traditional stepwise selection Nominal-to-ordinal conversion All
algorithms are intuitively justified and supported by the relevant
equations and explanatory material. The author also presents and
explains complete, highly commented source code. The example code
is in C++ and CUDA C but Python or other code can be substituted;
the algorithm is important, not the code that's used to write it.
What You Will Learn Combine principal component analysis with
forward and backward stepwise selection to identify a compact
subset of a large collection of variables that captures the maximum
possible variation within the entire set. Identify features that
may have predictive power over only a small subset of the feature
domain. Such features can be profitably used by modern predictive
models but may be missed by other feature selection methods. Find
an underlying hidden Markov model that controls the distributions
of feature variables and the target simultaneously. The memory
inherent in this method is especially valuable in high-noise
applications such as prediction of financial markets. Improve
traditional stepwise selection in three ways: examine a collection
of 'best-so-far' feature sets; test candidate features for
inclusion with cross validation to automatically and effectively
limit model complexity; and at each step estimate the probability
that our results so far could be just the product of random good
luck. We also estimate the probability that the improvement
obtained by adding a new variable could have been just good luck.
Take a potentially valuable nominal variable (a category or class
membership) that is unsuitable for input to a prediction model, and
assign to each category a sensible numeric value that can be used
as a model input. Who This Book Is For Intermediate to advanced
data science programmers and analysts.
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