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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.

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Clustering: which correlated feature to eliminate

I am trying to perform a simple k-means clustering of a sample population based on about 30 features. Some of the variables are highly correlated as measured by the Pearson correlation coefficient and ...
SAS2Python's user avatar
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Predict outputs based on a variable subset of inputs

To simplify this: I have 5 columns in my dataset -> A, B, C, D and E. I want the neural network to predict the rest of the outputs based on a subset of inputs. For example: Case #1 Inputs -> (A) ...
Sam's user avatar
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Feature engineering - filter out columns based on linear correlation result

Besides the other factors (such as domain knowledge), is there any rule of thumb or best practices to keep/remove features that were identified with high correlation between each other? The problem is,...
Echo's user avatar
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Calculating class-specific permutation feature importances for multilabel classification problem

I would like to apply the permutation feature importance technique to rank the features of a siamese network model that I trained. I am currently using this siamese network to perform some kind of ...
ashenflower's user avatar
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Periodic feature layer for Lotka-Volterra approximation

I am working with DeepXDE, a SciML library that can be used to solve differential equations. I came across this demo page for solving a Lotka-Volterra system. Since the solutions are known to be ...
explicitEllipticGroupAction's user avatar
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1 answer
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Methods of constructing input and ouput vectors in Reinforcement Learning with approximation function learning?

Give the one-hot state vector $\boldsymbol{x}(s)=[x_1(s),x_2(s)]^T$ and action spaces $A(s)=\{a_1,a_2\}$ for all $s$. In a course, I was taught to construct "stack" input vectors like $[x_{...
fermented_bean's user avatar
-2 votes
1 answer
68 views

the best choice to reduce a features vector

i have 1200 features highly correlated , and i want to reduce those number of features so the best choice is use feature selection or dimensionality reduction? and which method is the best in this ...
myriamkach's user avatar
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19 views

Sampling dollar bars for ML model of multiple tickers

I have a Neural Network model that provides predictions for the future returns of a portfolio comprising stocks and cryptocurrencies. The original model operates on standard time bars (sampled at a ...
apt45's user avatar
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Why is the feature direction chosen in the direction associated with largest eigenvalue of $\Sigma_T$ in case of more than two classes?

Why is the feature direction chosen in the direction associated with largest eigenvalue of $\Sigma_T$ in case of more than two classes? Please see the following.
DSPinfinity's user avatar
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16 views

Sparse linear discriminant analysis for regression problem?

So far, Linear Discriminant Analysis has beed used for classification problems http://proceedings.mlr.press/v38/wu15.pdf . I wonder if there are any ways to adapt it to regression problems?
PT_98's user avatar
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Handling Feature Selection Discrepancy in Image Classification Model

I have developed an image classification model that categorizes images into two classes (we'll say good and bad for the sake of example) based on a set of tags. To improve the model's performance, I ...
eszfgefr rgrer's user avatar
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Best feature engineering approach for interest-based age classification

I have a dataset which has users (rows) with the list of their interests (IABs), which looks like this ...
theodre7's user avatar
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1 answer
283 views

Is there a way to see the feature importance in deep learning (neural networks)? [closed]

For tree methods, I can plot the feature importance plot from tree.feature_importances_ in sklearn, is this achievable in deep learning (neural networks)? Is there ...
user900476's user avatar
2 votes
2 answers
413 views

Which correlated feature should be eliminated from a model?

BACKGROUND: There is a lot of information online about the problem of multicollinearity as it relates to machine learning and how to identify correlated features. However, I am still unclear on which ...
Snehal Patel's user avatar
1 vote
2 answers
63 views

Which type of neural network to use to classify data by which equation most likely generated it?

Problem Summary: Identify which equation a set of data was most likely generated from Problem Description: Let's say I have two different equations that are functions of variables X and Y and ...
Nova's user avatar
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26 views

How to decide which column has more weightage to output

As per Image we can see Column_A value is directly proportional to output, While Change in value of Column_B has no effects in output. So basically I want to know is there any algorithm where I can ...
Marques's user avatar
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1 answer
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Features for a Content-Based recommendation system

I'm working on a hybrid recommendation system (collaborative and content-based) for an online ordering/shopping app. So far I've managed to identify a data-source for the collaborative model (likely ...
S_Khan's user avatar
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1 answer
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Feature Engineering on transactional dataset clustering

I have a data set with transactions details from different business (roughly 1 thousand business entities). Each row is a transaction. The structure of the dataset is as follows: client_id Sex Age ...
Juan Ignacio Rojo's user avatar
2 votes
3 answers
77 views

Why my classification results are correlated with the proportionality of my data?

I'm facing a problem. I'm working on mixed data model with NN (MLP & Word Embedding). My results are not pretty good. And I observed that the proportionality of my data are corelated with my ...
Alexandre Juan's user avatar
1 vote
0 answers
136 views

What are Linear and Non-Linear Features of an image in the context of Convolutional Neural Network?

What features of image are linear or non-linear, any example ?
Shuai Xu's user avatar
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1 answer
246 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.
Mika's user avatar
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1 answer
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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 $k-$...
hanugm's user avatar
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1 vote
1 answer
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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 ...
Arthur Song's user avatar
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0 answers
200 views

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 ...
Roman's user avatar
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1 answer
95 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 ...
Nir's user avatar
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2 votes
0 answers
44 views

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 ...
Adnan Hussein's user avatar
4 votes
1 answer
112 views

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 ...
quest ions's user avatar
0 votes
1 answer
629 views

What is the impact of the number of features on the prediction power of a neural network?

What is the impact of the number of features on the prediction power of an ANN model (in general)? Does an increase in the number of features mean a more powerful prediction model (for approximation ...
Yazan Alatoom's user avatar
1 vote
0 answers
320 views

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 ...
mark mark's user avatar
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3 votes
1 answer
77 views

Is automated feature engineering a path to general AI?

I recently came across the featuretools package, which facilitates automated feature engineering. Here's an explanation of the package: https://towardsdatascience....
SuperCodeBrah's user avatar
1 vote
1 answer
169 views

Why does the training time of SVMs dramatically decrease after applying dimensionality reduction to the features?

Training an SVM with an RBF kernel model with c = 5.5 and gamma = 1.06, for a 5-class classification problem on the NSL-KDD ...
alighorbani's user avatar
1 vote
1 answer
100 views

When is adding a feature useless?

I'm building a model, where, from a feature set A, I want to predict a target set C. I need to understand if another feature set B, together with A, can improve my model performances, instead of using ...
Cla's user avatar
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0 answers
78 views

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 ...
sjv's user avatar
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2 votes
0 answers
30 views

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 ...
georgi koyrushki's user avatar
2 votes
0 answers
63 views

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 ...
Pluviophile's user avatar
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1 vote
0 answers
23 views

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 ...
Lenman147's user avatar
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0 answers
129 views

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 ...
ABIM's user avatar
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4 votes
2 answers
100 views

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 (...
Muhammad Maqsoodur Rehman's user avatar
0 votes
2 answers
35 views

Binarize ConvNet Feature vector [closed]

Given a pre-trained CNN model, I extract feature vector of 3450 reference images FV_R as follows: ...
doplano's user avatar
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1 vote
0 answers
44 views

When using neural networks, should I bin the continuous variables and apply some transformation before performing variable selection and modeling?

I come from a background of scorecard development using logistic regression. Steps involved there are: binning of continuous variables into intervals (eg age can be binned into 10-15 years, 15-20 ...
user3741952's user avatar
4 votes
1 answer
4k views

What is the $\ell_{2, 1}$ norm?

I'm reading this paper and it says: In this paper, we present a multi-class embedded feature selection method called as sparse optimal scoring with adjustment (SOSA), which is capable of addressing ...
Gyntonic's user avatar
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1 vote
0 answers
62 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 ...
Alex's user avatar
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2 votes
1 answer
1k views

How does the decision tree implicitly do feature selection?

I was talking with an ex-fellow worker and he told me that the decision tree implicitly applies a feature selection. He told me that the most important feature is higher in the tree because of the ...
jennifer ruurs's user avatar
2 votes
1 answer
71 views

Should I use my redundant feature as an auxiliary output or as another input feature?

For example, given a face image, and you want to predict the gender. You also have age information for each person, should you feed the age information as input or should you use it as auxiliary ...
offchan's user avatar
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1 vote
2 answers
226 views

Can neural networks be used to find features importance?

I am wondering if I can use neural networks to find features importances in similar manner as it can be done for random forests or decision trees and if so, how to do it? I would like to use it on ...
GKozinski's user avatar
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1 vote
0 answers
38 views

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 ...
JunjieChen's user avatar
3 votes
0 answers
163 views

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 ...
ATidedHumour's user avatar
1 vote
2 answers
53 views

What features should a dataset to predict monthly retail sales for a motorcycle spare parts shop have?

I am making an AI model to predict monthly retail sales of a motor cycle spare parts shop, for that to be possible I have to first create a dataset. The problem I am facing is what features should the ...
Kaawya's user avatar
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0 votes
2 answers
68 views

How to analyze data before going for machine learning training?

For example, I have the following csv: training.csv I want to know how I can determine which column will be the best feature for getting the output prediction before I go for machine training. Please ...
Jaffer Wilson's user avatar
1 vote
1 answer
121 views

When should I use feature learning as opposed to feature engineering?

With the advancement of deep learning and a few others automated features learning techniques, manual feature engineering started becoming obsolete. Any suggestion on when to use manual feature ...
Ruchit Dalwadi's user avatar