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.
56
questions
0
votes
1
answer
37
views
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_{...
-2
votes
1
answer
53
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 ...
0
votes
0
answers
14
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 ...
- 123
0
votes
0
answers
11
views
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.
- 493
0
votes
0
answers
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?
- 1
0
votes
0
answers
17
views
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 ...
0
votes
0
answers
19
views
What is the main difference between class label prediction through Machine Learning and Conditional Label generation that can be done on raw data?
I have understood that machine learning helps us to classify various instances using the labeled data, but why do we need machine learning when we obtain the same results through generating labels by ...
- 11
0
votes
0
answers
9
views
Do we have reversed recursive feature elimination (RFE)?
We usually use RFE to eliminate less-important features to improve model performance.
I wonder if we have a reversed RFE, which intentionally remove important features?
The intention for this is to ...
- 111
1
vote
0
answers
26
views
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
...
- 11
0
votes
0
answers
23
views
Is there a test to determine if a feature space is sufficient for a classification problem?
I am working with GNNs for a node classification problem, and I am only able to achieve about 50% accuracy for the training set. I am not able to overfit the model to the training data. This makes me ...
- 173
0
votes
1
answer
174
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 ...
- 121
0
votes
0
answers
22
views
Fast feature elimination in python on GPU
I have a dataset with millions of samples and tens of thousands of features. Most of these features are highly correlated with each other. However, eliminating features based on a correlation ...
2
votes
2
answers
189
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 ...
- 922
0
votes
1
answer
50
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 ...
- 133
2
votes
0
answers
23
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 ...
- 21
1
vote
1
answer
43
views
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 ...
- 11
1
vote
1
answer
263
views
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
...
2
votes
3
answers
76
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 ...
1
vote
0
answers
124
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 ?
- 11
0
votes
2
answers
162
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.
- 341
1
vote
1
answer
168
views
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 $k-$...
- 3,612
1
vote
1
answer
96
views
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 ...
- 11
0
votes
0
answers
177
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 ...
- 51
1
vote
1
answer
92
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 ...
- 11
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 ...
4
votes
1
answer
108
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 ...
- 384
0
votes
1
answer
497
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 ...
1
vote
0
answers
265
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 ...
- 763
3
votes
1
answer
67
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....
- 273
1
vote
1
answer
158
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 ...
- 13
1
vote
1
answer
84
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 ...
- 23
0
votes
0
answers
72
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 ...
- 1
2
votes
0
answers
27
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 ...
2
votes
0
answers
61
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 ...
- 1,243
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 ...
- 11
0
votes
0
answers
122
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 ...
- 535
4
votes
2
answers
99
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 (...
0
votes
2
answers
34
views
Binarize ConvNet Feature vector [closed]
Given a pre-trained CNN model, I extract feature vector of 3450 reference images FV_R as follows:
...
- 299
1
vote
0
answers
39
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 ...
- 11
3
votes
1
answer
3k
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 ...
- 133
1
vote
0
answers
54
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 ...
- 111
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 ...
- 579
2
votes
1
answer
67
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 ...
- 325
1
vote
2
answers
190
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 ...
- 1,240
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 ...
- 111
3
votes
0
answers
148
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 ...
- 125
1
vote
2
answers
47
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 ...
- 13
0
votes
2
answers
63
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 ...
- 143
1
vote
1
answer
112
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 ...
- 325
3
votes
2
answers
85
views
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 ...
- 131