LEADER 05171nam 22006375 450 001 9910409993103321 005 20200704024001.0 010 $a1-4842-5988-2 024 7 $a10.1007/978-1-4842-5988-7 035 $a(CKB)4100000011278640 035 $a(MiAaPQ)EBC6221628 035 $a(DE-He213)978-1-4842-5988-7 035 $a(CaSebORM)9781484259887 035 $a(PPN)248598929 035 $a(OCoLC)1199337263 035 $a(OCoLC)on1199337263 035 $a(EXLCZ)994100000011278640 100 $a20200605d2020 u| 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aModern Data Mining Algorithms in C++ and CUDA C $eRecent Developments in Feature Extraction and Selection Algorithms for Data Science /$fby Timothy Masters 205 $a1st ed. 2020. 210 1$aBerkeley, CA :$cApress :$cImprint: Apress,$d2020. 215 $a1 online resource (ix, 228 pages) 300 $aIncludes index. 311 $a1-4842-5987-4 327 $a1. Introduction -- 2. Forward Selection Component Analysis -- 3. Local Feature Selection -- 4. Memory in Time Series Features -- 5. Stepwise Selection on Steroids -- 6. Nominal-to-Ordinal Conversion. 330 $aAs 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. You will: 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. 606 $aData mining 606 $aComputer software 606 $aStatistics  606 $aProgramming languages (Electronic computers) 606 $aData Mining and Knowledge Discovery$3https://scigraph.springernature.com/ontologies/product-market-codes/I18030 606 $aProfessional Computing$3https://scigraph.springernature.com/ontologies/product-market-codes/I29000 606 $aStatistics, general$3https://scigraph.springernature.com/ontologies/product-market-codes/S0000X 606 $aProgramming Languages, Compilers, Interpreters$3https://scigraph.springernature.com/ontologies/product-market-codes/I14037 615 0$aData mining. 615 0$aComputer software. 615 0$aStatistics . 615 0$aProgramming languages (Electronic computers). 615 14$aData Mining and Knowledge Discovery. 615 24$aProfessional Computing. 615 24$aStatistics, general. 615 24$aProgramming Languages, Compilers, Interpreters. 676 $a006.312 700 $aMasters$b Timothy$4aut$4http://id.loc.gov/vocabulary/relators/aut$0105163 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910409993103321 996 $aModern Data Mining Algorithms in C++ and CUDA C$92025660 997 $aUNINA