2021년 2월 17일 (수) 08:14에 Pythagoras0님의 편집

2021-02-17T08:14:40Z

Pythagoras0: /* 메타데이터 */ 새 문단

2020-12-26T12:21:09Z

메타데이터: 새 문단

Pythagoras0: /* 노트 */ 새 문단

2020-12-21T09:53:23Z

노트: 새 문단

새 문서

== 노트 ==

===위키데이터===
* ID : [https://www.wikidata.org/wiki/Q446488 Q446488]
===말뭉치===
# In this article, I will focus on one of the 2 critical parts of getting your models right – feature selection.<ref name="ref_6a9877c4">[https://www.analyticsvidhya.com/blog/2016/12/introduction-to-feature-selection-methods-with-an-example-or-how-to-select-the-right-variables/ Feature Selection Methods]</ref>
# This is just an example of how feature selection makes a difference.<ref name="ref_6a9877c4" />
# I believe that his article has given you a good idea of how you can perform feature selection to get the best out of your models.<ref name="ref_6a9877c4" />
# These are the broad categories that are commonly used for feature selection.<ref name="ref_6a9877c4" />
# Plenty of feature selection methods are available in literature due to the availability of data with hundreds of variables leading to data with very high dimension.<ref name="ref_98813e87">[https://www.sciencedirect.com/science/article/pii/S0045790613003066 A survey on feature selection methods ☆]</ref>
# We also apply some of the feature selection techniques on standard datasets to demonstrate the applicability of feature selection techniques.<ref name="ref_98813e87" />
# Feature Selection is the process of selecting out the most significant features from a given dataset.<ref name="ref_6c622de6">[https://www.datacamp.com/community/tutorials/feature-selection-python (Tutorial) Feature Selection in Python]</ref>
# You got an informal introduction to Feature Selection and its importance in the world of Data Science and Machine Learning.<ref name="ref_6c622de6" />
# The importance of feature selection can best be recognized when you are dealing with a dataset that contains a vast number of features.<ref name="ref_6c622de6" />
# Sometimes, feature selection is mistaken with dimensionality reduction.<ref name="ref_6c622de6" />
# The logic behind using correlation for feature selection is that the good variables are highly correlated with the target.<ref name="ref_617b1053">[https://www.analyticsvidhya.com/blog/2020/10/feature-selection-techniques-in-machine-learning/ Feature Selection Techniques in Machine Learning]</ref>
# The feature selection process is based on a specific machine learning algorithm that we are trying to fit on a given dataset.<ref name="ref_617b1053" />
# This method works exactly opposite to the Forward Feature Selection method.<ref name="ref_617b1053" />
# This is the most robust feature selection method covered so far.<ref name="ref_617b1053" />
# Feature Selection is one of the core concepts in machine learning which hugely impacts the performance of your model.<ref name="ref_4fbb8770">[https://towardsdatascience.com/feature-selection-techniques-in-machine-learning-with-python-f24e7da3f36e Feature Selection Techniques in Machine Learning with Python]</ref>
# In one of related works, a filter-based method has been introduced for use in online stream feature selection applications.<ref name="ref_cf300b40">[https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-019-2754-0 FeatureSelect: a software for feature selection based on machine learning approaches]</ref>
# This method has acceptable stability and scalability, and can also be used in offline feature selection applications.<ref name="ref_cf300b40" />
# Feature selection for linear data types has also been studied, in a work that provides a framework and selects features with maximum relevance and minimum redundancy.<ref name="ref_cf300b40" />
# In a separate study, a feature selection method was proposed in which both unbalanced and balanced data can be classified, based on a genetic algorithm.<ref name="ref_cf300b40" />
# An important distinction to be made in feature selection is that of supervised and unsupervised methods.<ref name="ref_188a1277">[https://machinelearningmastery.com/feature-selection-with-real-and-categorical-data/ How to Choose a Feature Selection Method For Machine Learning]</ref>
# Unsupervised feature selection techniques ignores the target variable, such as methods that remove redundant variables using correlation.<ref name="ref_188a1277" />
# Wrapper feature selection methods create many models with different subsets of input features and select those features that result in the best performing model according to a performance metric.<ref name="ref_188a1277" />
# Finally, there are some machine learning algorithms that perform feature selection automatically as part of learning the model.<ref name="ref_188a1277" />
# This post is about some of the most common feature selection techniques one can use while working with data.<ref name="ref_9e03d499">[https://towardsdatascience.com/the-5-feature-selection-algorithms-every-data-scientist-need-to-know-3a6b566efd2 The 5 Feature Selection Algorithms every Data Scientist should know]</ref>
# Removing features with low variance¶ VarianceThreshold is a simple baseline approach to feature selection.<ref name="ref_4f7b127d">[http://scikit-learn.org/stable/modules/feature_selection.html 1.13. Feature selection — scikit-learn 0.23.2 documentation]</ref>
# Univariate feature selection¶ Univariate feature selection works by selecting the best features based on univariate statistical tests.<ref name="ref_4f7b127d" />
# GenericUnivariateSelect allows to perform univariate feature selection with a configurable strategy.<ref name="ref_4f7b127d" />
# Feature selection using SelectFromModel¶ SelectFromModel is a meta-transformer that can be used along with any estimator that has a coef_ or feature_importances_ attribute after fitting.<ref name="ref_4f7b127d" />
# Feature extraction creates new features from functions of the original features, whereas feature selection returns a subset of the features.<ref name="ref_3f179fe9">[https://en.wikipedia.org/wiki/Feature_selection Feature selection]</ref>
# Feature selection techniques are often used in domains where there are many features and comparatively few samples (or data points).<ref name="ref_3f179fe9" />
# A feature selection algorithm can be seen as the combination of a search technique for proposing new feature subsets, along with an evaluation measure which scores the different feature subsets.<ref name="ref_3f179fe9" />
# Embedded methods are a catch-all group of techniques which perform feature selection as part of the model construction process.<ref name="ref_3f179fe9" />
# Feature selection is the process by which a subset of relevant features, or variables, are selected from a larger data set for constructing models.<ref name="ref_27829565">[https://deepai.org/machine-learning-glossary-and-terms/feature-selection Feature Selection]</ref>
# Variable selection, attribute selection or variable subset selection are all other names used for feature selection.<ref name="ref_27829565" />
# The main focus of feature selection is to choose features that represent the data set well by excluding redundant and irrelevant data.<ref name="ref_27829565" />
# Feature selection is useful because it simplifies the learning models making interpretation of the model and the results easier for the user.<ref name="ref_27829565" />
# Feature selection is the study of algorithms for reducing dimensionality of data to improve machine learning performance.<ref name="ref_2bf3f818">[https://link.springer.com/10.1007/978-0-387-30164-8_306 Feature Selection]</ref>
# Feature selection is commonly used in applications where original features need to be retained.<ref name="ref_2bf3f818" />
# These models are thought to have built-in feature selection: ada , AdaBag , AdaBoost.<ref name="ref_5198045d">[https://topepo.github.io/caret/feature-selection-overview.html 18 Feature Selection Overview]</ref>
# In many cases, using these models with built-in feature selection will be more efficient than algorithms where the search routine for the right predictors is external to the model.<ref name="ref_5198045d" />
# Apart from models with built-in feature selection, most approaches for reducing the number of predictors can be placed into two main categories.<ref name="ref_5198045d" />
# The crucial role played by the feature selection step has led many researchers to innovate and find different approaches to address this issue.<ref name="ref_25a7bbbe">[https://journalofbigdata.springeropen.com/articles/10.1186/s40537-019-0241-0 Feature selection methods and genomic big data: a systematic review]</ref>
# The initial feature selection type is the filter methods, in which the algorithm selecting relevant and non-redundant features in the data set is actually independent of the used classifier.<ref name="ref_25a7bbbe" />
# Many bioinformatics researchers have shown interest in this particular type of feature selection methods due to the simplicity of its implementation, its low computational cost and its speed.<ref name="ref_25a7bbbe" />
# Then, using real data they show evidence that their wrapper feature selection leads to higher predictive accuracy than mRMR.<ref name="ref_25a7bbbe" />
# We can view feature selection as a method for replacing a complex classifier (using all features) with a simpler one (using a subset of the features).<ref name="ref_93b202b6">[https://nlp.stanford.edu/IR-book/html/htmledition/feature-selection-1.html Feature selection]</ref>
# The basic feature selection algorithm is shown in Figure 13.6 .<ref name="ref_93b202b6" />
# This section mainly addresses feature selection for two-class classification tasks like China versus not-China.<ref name="ref_93b202b6" />
# Often feature selection based on a filter method is part of the data preprocessing and in a subsequent step a learning method is applied to the filtered data.<ref name="ref_b6d30380">[https://mlr.mlr-org.com/articles/tutorial/feature_selection.html Feature Selection]</ref>
# In each resampling iteration feature selection is carried out on the corresponding training data set before fitting the learner.<ref name="ref_b6d30380" />
# The software has been implemented to automate all machine learning steps, including data pre-processing, feature selection, model selection, and performance evaluation.<ref name="ref_f6598aa6">[https://www.frontiersin.org/articles/10.3389/fgene.2019.00452/full Large-Scale Automatic Feature Selection for Biomarker Discovery in High-Dimensional OMICs Data]</ref>
# In this section, we describe the program procedures separated in three main components: preprocessing, feature selection and model selection.<ref name="ref_f6598aa6" />
# Preprocessing and feature selection procedures are fully parallelizable, When all features-optimized models are computed, the model selection starts.<ref name="ref_f6598aa6" />
# This optimization procedure performed on feature selection either maximize or minimize the criterion, depending if it measures a performance or an error, respectively.<ref name="ref_f6598aa6" />
# Variable and feature selection have become the focus of much research in areas of application for which datasets with tens or hundreds of thousands of variables are available.<ref name="ref_0021775c">[https://dl.acm.org/citation.cfm?id=944919.944968 An introduction to variable and feature selection]</ref>
# Dimensionality reduction is another concept that newcomers tend to lump together with feature selection.<ref name="ref_676d35be">[https://heartbeat.fritz.ai/hands-on-with-feature-selection-techniques-an-introduction-1d8dc6d86c16 Hands-on with Feature Selection Techniques: An Introduction]</ref>
# Feature selection is a method of selecting a subset of all features provided with observations data to build the optimal Machine Learning model.<ref name="ref_554859e5">[https://blog.netcetera.com/feature-selection-in-details-217bac0ed98d Feature selection is a method of selecting a subset of all features provided with observations data to build the optimal Machine Learning model.]</ref>
# Embedded methods perform feature selection during the model training process.<ref name="ref_554859e5" />
# Feature selection using linear models assumes multivariant dependency of the target from values of available features, and values of available features are normally distributed.<ref name="ref_554859e5" />
# In this blog post, we shall continue our discussion further on “Feature Selection in Machine Learning”.<ref name="ref_f5d9c43a">[https://medium.com/@mehulved1503/feature-selection-in-machine-learning-variable-ranking-and-feature-subset-selection-methods-89b2896f2220 Feature Selection in Machine Learning: Variable Ranking and Feature Subset Selection Methods]</ref>
# In the previous blog post, I’d introduced the the basic definitions, terminologies and the motivation in Feature Selection.<ref name="ref_f5d9c43a" />
# Feature selection mythologies fall into three general classes: intrinsic (or implicit) methods, filter methods, and wrapper methods.<ref name="ref_2cac3ad4">[http://www.feat.engineering/classes-of-feature-selection-methodologies.html Feature Engineering and Selection: A Practical Approach for Predictive Models]</ref>
# Intrinsic methods have feature selection naturally incorporated with the modeling process.<ref name="ref_2cac3ad4" />
# Whereas filter and wrapper methods work to marry feature selection approaches with modeling techniques.<ref name="ref_2cac3ad4" />
# If the data are better fit by a non-intrinsic feature selection type of model, then predictive performance may be sub-optimal when all features are used.<ref name="ref_2cac3ad4" />
# This article describes how to use the Filter Based Feature Selection module in Azure Machine Learning designer.<ref name="ref_d21cedc6">[https://docs.microsoft.com/en-us/azure/machine-learning/algorithm-module-reference/filter-based-feature-selection Filter Based Feature Selection: Module reference - Azure Machine Learning]</ref>
# In general, feature selection refers to the process of applying statistical tests to inputs, given a specified output.<ref name="ref_d21cedc6" />
# The Filter Based Feature Selection module provides multiple feature selection algorithms to choose from.<ref name="ref_d21cedc6" />
# When you use the Filter Based Feature Selection module, you provide a dataset and identify the column that contains the label or dependent variable.<ref name="ref_d21cedc6" />
# Good feature selection eliminates irrelevant or redundant columns from your dataset without sacrificing accuracy.<ref name="ref_25000733">[https://www.datarobot.com/wiki/feature-selection/ Feature Selection for Machine Learning]</ref>
# Automated feature selection.<ref name="ref_25000733" />
# The process of feature selection can be briefly described as follows.<ref name="ref_ca66cf16">[https://www.nature.com/articles/s41598-019-53471-0 Feature selection with the Fisher score followed by the Maximal Clique Centrality algorithm can accurately identify the hub genes of hepatocellular carcinoma]</ref>
# To further evaluate the performance of the Fisher score algorithm, a series of control feature selection algorithms were utilized to select feature genes from the current integrated HCC dataset.<ref name="ref_ca66cf16" />
# According to Applied Predictive Modeling, 2013, feature selection is primarily focused on removing non-informative or redundant predictors from the model.<ref name="ref_be82161c">[https://codeburst.io/what-is-feature-selection-in-machine-learning-and-how-is-it-used-43042a0683c1 What is Feature Selection in Machine Learning and How is it Used?]</ref>
# So, given the fact that more and more features are becoming available for machine learning projects, feature selection algorithms are increasingly growing in significance.<ref name="ref_5568189c">[https://www.explorium.ai/blog/demystifying-feature-selection-filter-vs-wrapper-methods/ Data Science Feature Selection: Filter vs Wrapper Methods l Explorium]</ref>
# My team is responsible for locating algorithms and feature selection strategies.<ref name="ref_5568189c" />
# In order to examine the two feature selection methodologies, let’s take a look at a small sample of our Melbourne prices dataset.<ref name="ref_5568189c" />
# At this point, all the generated features will be clean and normalized, before being thrown into the feature selection phase.<ref name="ref_5568189c" />
# Before conducting these model developments, feature selection was applied in order to select the most important input parameters for PPV.<ref name="ref_e3a45a7f">[https://www.mdpi.com/2076-3417/10/3/869 A Combination of Feature Selection and Random Forest Techniques to Solve a Problem Related to Blast-Induced Ground Vibration]</ref>
# In this study, we propose a feature selection method for text classification based on independent feature space search.<ref name="ref_fdf96d52">[https://www.hindawi.com/journals/mpe/2020/6076272/ A New Feature Selection Method for Text Classification Based on Independent Feature Space Search]</ref>
# Therefore, the dimension reduction methods have been proposed to solve this problem, including feature extraction and feature selection.<ref name="ref_fdf96d52" />
# In this paper, we propose a novel and effective idea of feature selection and use the diagrams to illustrate the difference between this method and the general feature selection method.<ref name="ref_fdf96d52" />
# Figure 2 shows the process diagram of the new feature selection method, namely, the RDTFD method, step ① represents all features are added to the original features set.<ref name="ref_fdf96d52" />
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 * ID :  [https://www.wikidata.org/wiki/Q446488 Q446488]

← 이전 판		2020년 12월 26일 (토) 12:21 판
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Feature selection - 편집 역사

2021년 2월 17일 (수) 08:14에 Pythagoras0님의 편집

Pythagoras0: /* 메타데이터 */ 새 문단

Pythagoras0: /* 노트 */ 새 문단