What is feature selection in artificial neural networks?
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| Papers (10) | Insight |
|---|---|
| Experimental results show that very few hidden neurons are required for feature selection as artificial neural networks are only used to assess the quality of an individual, which is a chosen subset of features. | |
07 Nov 2005 46 Citations | Feature selection in the forecaster based on artificial neural network is a well-researched problem, which can improve the network performance and speed up the training of the network. |
01 Jan 2000 7 Citations | It is illustrated that WCR based neural network feature selection may be very effective in reducing model complexity for classification modelling via neural networks. |
108 Citations | This paper describes a novel feature subset selection algorithm, which utilizes a genetic algorithm (GA) to optimize the output nodes of trained artificial neural network (ANN). |
13 Aug 2007 64 Citations | Additionally, the book highlights the important feature selection problem, which baffles many neural networks practitioners because of the difficulties handling large datasets. |
| Additionally, the feature chosen process makes the Artificial Neural Networks classifier straightforward and more efficient. | |
171 Citations | We propose a feature selection technique for finding an optimum feature subset that enhances the classification accuracy of neural network classifiers. |
26 May 2013 | Feature selection reduces the number of features to a manageable number for training simpler algorithms such as logistic regression, but this number is still too large for more complex algorithms such as neural networks. |
25 Jul 2004 47 Citations | With the feature selection process proposed, it is possible to reduce the number of input features significantly, which is very important due to the fact that the neural networks can effectively be prevented from overfitting. |
| It is shown that the instance and feature selection prevents the overfitting of neural networks. |
Related Questions
Why is feature selection important?5 answersFeature selection is crucial in machine learning as it helps in improving model performance by selecting the most relevant features while reducing noise and redundancy. Effective feature selection methods, such as Filter Feature Selection, Feature Selection with Annealing (FSA), and Distance Correlation (DisCo), aid in enhancing forecasting accuracy, reducing computation time, and improving interpretability. Feature selection also plays a vital role in selecting features from large datasets, optimizing model parameters, and improving the efficiency of learning algorithms. By choosing the most informative features, feature selection contributes to better model generalization, faster training times, and increased model robustness.
Features selection methods in data science ?5 answersFeature selection methods in data science are used to reduce the complexity of high-dimensional datasets and improve the performance of machine learning models. These methods aim to eliminate redundant and non-informative features from the dataset. Various algorithms such as CFS, CAE, IGE, GRE, and WSE have been used for feature selection, and their performance has been evaluated using models like Naive Bayes and support vector machine (SVM). Wrapper methods, such as Sequential Feature Selection, are used to select features that contribute highly to the accuracy of the model. Feature selection can be done in offline mode, where the entire dataset is available, or in online mode, where data is processed one instance at a time. Online feature selection algorithms, such as the proposed Sparse Gradient method, can efficiently handle large datasets and produce results swiftly.
How to select features for a machine learning model?5 answersFeature selection is a crucial step in building machine learning models. It involves choosing the most relevant and informative features from a dataset to improve model performance and interpretability. Various methods have been proposed for feature selection, including causal feature selection, distribution-free feature selection, and stability-based feature selection. Causal feature selection approaches, such as M-PC1 and PCMCI, use causal discovery algorithms to identify causal drivers from multiple time series datasets. Distribution-free feature selection methods, like Data Splitting Selection (DSS), control the False Discovery Rate (FDR) while maintaining high power. Stability-based feature selection focuses on the stability of selected features and measures their performance and robustness. These approaches help in reducing the dimensionality of datasets, improving model accuracy, and building simpler models.
What are Filter-based approaches or methods in feature selection for machine learning?5 answersFilter-based approaches or methods in feature selection for machine learning are techniques that aim to improve the accuracy of classifiers and reduce computational time by selecting relevant attributes and removing irrelevant, inappropriate, and correlated variables. These methods are independent of the learning algorithm and can be used to address issues such as overfitting and high computational cost. Some examples of filter-based feature selection techniques include Symmetrical Uncertainty, Information Gain, Chi-square, ReliefF, Spearman Correlation, Fisher score, Pearson Correlation, Count based, Kendall Correlation, and Mutual Information. These techniques have been applied in various domains, such as time series forecasting, customer analysis, and medical diagnosis, with promising results in terms of improved performance and efficiency.
What is different feature selection in machine learning?3 answersFeature selection in machine learning refers to the process of choosing a subset of relevant features from the original set of features. This is done to improve the performance and accuracy of machine learning algorithms. Feature selection helps in reducing the dimensionality of the data, which in turn reduces the complexity of the model and improves its interpretability. It also helps in reducing processing costs and improving learning accuracy. Various feature selection methods have been proposed, including quadratic unconstrained optimization (QUBO) methods, sequential forward selection (SFS), backward elimination (BE), recursive feature elimination (RFE), correlation-based methods, and hybrid approaches. These methods aim to identify the most informative and discriminative features for building robust machine learning models.
What are the different types of feature selection in machine learning?3 answersFeature selection is an important task in machine learning to choose the most relevant features for building robust models. Different types of feature selection methods have been adopted in the literature. Some common methods include Sequential Forward Selection (SFS), Backward Elimination (BE), Recursive Feature Elimination (RFE), correlation, one-way ANOVA test, and hybrid methods. Another approach is to treat feature selection as a quadratic unconstrained optimization problem (QUBO), which can be solved using classical numerical methods or within a quantum computing framework. Additionally, there are feature selection algorithms like Forward Feature Selection Algorithm (FFS) and Sequential Input Selection Algorithm (SISAL) that are used in machine learning. These methods aim to reduce the number of features and improve the performance of the models by selecting the most informative ones.