Questions tagged [feature-selection]

For questions related to the concept of feature selection (also known as variable selection or attribute selection), which is the process of selecting a subset of relevant features (a.k.a. variables or predictors) for use in model construction.

22 questions with no upvoted or accepted answers
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How should I select the features for predicting diseases (in particular when patients specify their health issues)?

My aim is to train a model for predicting diseases. Now, according to this Wikipedia article, diseases are classified based on the following criteria in general: Causes (of the disease) Pathogenesis (...
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Feature Selection using Monte Carlo Tree Search

I'm trying to tackle the problem of feature selection as an RL problem, inspired by the paper Feature Selection as a One-Player Game. I know Monte-Carlo tree search (MCTS) is hardly RL. So, I used ...
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Problem extracting features from convolutional layer where the dimensions are big for feature maps

I have trained a convolutional neural network on images to detect emotions. Now I need to use the same network to extract features from the images and use them to train an LSTM. The problem is: the ...
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In the Binary Flower Pollination Algorithm (using the sigmoid function), is it possible that no feature is selected?

I'm trying to use the Binary Flower Pollination Algorithm (BFPA) for feature selection. In the BFPA, the sigmoid function is used to compute a binary vector that represents whether a feature is ...
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How does uniform offset tiling work with function approximation?

I get the fundamental idea of how tilings work, but, in Barton and Sutton's book, Reinforcement Learning: An Introduction (2nd edition), a diagram, on page 219 (figure 9.11), showing the variations of ...
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Visualisation for Features to Predict Timeseries Data

I have a course assignment to use an LSTM to predict the movement directions of stock prices. One of the things I am asked to do is provide a visualization to compare the predictive powers of a set of ...
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How can Cat Swarm Algorithm (CSO) used for feature selection?

Cat swarm optimization (CSO) is a novel metaheuristic for evolutionary optimization algorithms based on swarm intelligence which proposed in 2006. See Feature Selection of Support Vector Machine Based ...
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What are examples of commonly used feature and readout maps?

It is well-known that deep feedforward networks can approximate any continuous function from $\mathbb{R}^k$ to $\mathbb{R}^l$, (uniformly on compacts). However, in practice feature maps are ...
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How many singular vectors do we need to calculate for SVD?

In the geometrical interpretation of SVD, the data points that we have need to be imagined as points in high dimensional space (say $d$-dimensional space). But we need to find a hyperplane in $k-$...
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1answer
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Selecting features for a neural network: is it redundant to have a feature that is an average (or max, or min) of some other features

I'm trying to create a neural network that would able to look at the current price of a crypto asset and classify between a "BUY", "SELL" or "HOLD". So far for my input ...
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Does the order in which the features are concatenated to create the state (or observation) matter?

I'm experimenting with an RL agent that interacts with the following environment. The learning algorithm is double DQN. The neural network represents the function from state to action. It's build with ...
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Feature extraction for exponentially damped signals

I am looking into exponentially damped signals where it is a stationary signal (after implementing the Adfuller statistical test) and I would like to look into how can I extract meaningful features ...
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37 views

Interpretability of feature weights from Gaussian process classifier

Suppose I trained a Gaussian process classifier with a linear kernel (using GPML toolbox) and got some feature weights for each input feature. My question is then: Does it/When does it make sense ...
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How to combine features with different temporal scale in machine learning

We have various types of data features with different temporal scale. For example, some of them describe the state per second while others may describe the state per day or per month from another ...
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2answers
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Feature selection of an acoustic voice dataset for classification

I'm trying to gain some insight into acoustic voice data composed of 19 features. I want to understand what features contribute most for classification. ADDED: Most features are related with the ...
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1answer
29 views

Is there a way to select the subset of most important features using PCA?

Is there a way to select the most important features using PCA? I am not looking for the principal components with the highest scores but a subset of the original features.
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How can I select features for a symbolic regression problem to be solved with genetic programming?

I want to solve a symbolic regression problem with genetic programming. My dataset is similar to this one, but I have 30 features, and I want to use only the most sensitive features. I found this ...
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Is there a theoretical or recommended number of estimators for Random Forest in Feature Selection?

I am using a RandomForestRegressor as an estimator in the SelectFromModel object (sklearn) ...
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1answer
65 views

How to predict the best from a set of messages - best practice

Suppose I have a set of messages A,B,C,D and I want to produce the best message for a website user at a given time. For training I plan to show random users a random single message [A/B/C/D] and fill ...
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What is the best way to do classification using both text and numerical data?

For my BSc thesis I am trying to classify asset price direction (up/down/neutral) using numerical features and Swedish text. The text is short financial news (ca 50 words each) that have sentiment ...
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How to obtain SHAP values

I want to obtain SHAP values with kernel SHAP without using python but I don't really understand the algorithm. If I have a kNN classifier, do I have to run the classifier for all the coalitions ...