Questions tagged [feature-extraction]
Variables (used for prediction or explication) used in regression or regression-like models (like clustering, discrimination). Use this tag for questions about constructing such variables or selecting the best among them.
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Dimensionality reduction for feature extraction when missing some feature values
I have two questions:
1-Which method is appropriate for dimensionality reduction for feature extraction when missing some feature values?
2-Which textbook is the best source for the answer in (1)?
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How do I get my Neural network to ignore certain values?
I was wondering if there was a way that I can get my CNN encoder-decoder neural network to completely ignore certain values in my data (2d images).
There are some pixel values of 0 that never change ...
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Using extracted feature vector to perform zero shot detection
I've developed a deep learning model trained from scratch on fruits and vegetables. However, as the data is limited, I can only cover a few different types of fruits and vegetables with the model ...
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Is it a good idea to combine fine tuning and feature extraction techniques?
I have a normal/tumor medical images dataset and, for the same patients, also the relative genomics, and my goal is to predict if a patient has a tumor by combining all the information.
To achieve ...
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Would a neural network trained on extracted features have the same accuracy as a full network with frozen layers?
Let's say that I train two neural networks on the exact same dataset.
The first network is a VGG19 model with frozen convolutional layers so only the top dense ...
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Regression with a feature which has its own depth
I'm relatively new to ML/Statistical Analysis, and I'm facing a dataset structured like this
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How can I transform a sequence into features
When Machine Learning libraries don't support categorical features those features can be one-hot encoded into a series of binary feature columns. I have a feature that represents a sequence or ...
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Using partially defined features in an unified deep learning model
Suppose we have two types of feature A and B. A is defined for all kinds of samples while B is only defined for some of the samples. Here, B is partially defined does not mean B is missing value (such ...
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How to decide which method to use TFIDF, or BOW
In a huge dataset for NLP it is taking very long time to classify my dataset
therefore, trying each feature extraction method separetly is time consuming and not effecient.
Is there a way that can ...
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How many words should be taken as features in a ML problem?
I would like to ask you how many words should be taken as features in a ML program.
For example, if I have 30000 distinct words to make a vocabulary, what would a good number be?
I am currently ...
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How to merge multiple filters of a layer to a single filter in convolutional neural network?
In a convolutional neural network (CNN), the layer weights are learnt such that they extract meaningful features from the data. For each layer, can we merge multiple filters into a single filter after ...
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Cannot understand feature extraction
I'm following an AI course and we've just entered the deep learning chapter. Speaking about the difference between classic machine learning models and deep learning, it turns out one of the most ...
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Creating features from raw accelerometer data
I have a dataset containing raw 3-axis accelerometer data collected from a users lower leg and I want to create a classification model (as simple as possible) that detects if the user is sat down or ...
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ValueError: Could not guess the value column! Please hand it to the function as an argument
I'm using the tsfresh library to extract features on my data but I keep getting an error:
...
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Matching Data Text of Two Place with Exception [closed]
I have data of two places name and it's address in a row and i have to match it. Data is text type, I have read, it have to convert to numeric type that generated by the text. I extracted the numeric ...
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I need help in PCA results using WEKA Tool [closed]
I'm working on an experiment using KDD'99 cupset I have 42 features. the paper I 'm comparing with concludes that 3 features with precision ..% ok are the best subset to identify the attack X.
In my ...
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Use feature-importance to decide what features to increase to increase target
(Please suggest another title for this question if you like - I find it rather difficult to phrase)
Say I have an ice-cream stand and I record 3 features of my customers ...
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Selecting only a certain number of top features using tsfresh
How can I select the top n features of time series dataset using tsfresh? Can I decide the number of top features I would like to extract?
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What is meta- data and meta features?
I want to know what is metadata and what is meant by meta features?
When I google Meta Features what I get is feature selection tool called "Meta-Feature". What is the function of feature ...
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Sneakers representation learning
I am trying to make a model which would take an image of shoes as an input and output a meaningful N-dimensional embedding of the shoes, so that they could be searchable/comparable/clustered and used ...
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Problem extracting words from dataframe
I have the following dataset which is a .json file:
and I would like to get the first word for every string inside lista_asm, so I would like to get: jmp,push,...
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Is the result of feature extraction a feature representation?
If a use a feature extraction method on images, do I then get a feature representation or is there a different meaning behind feature representation?
To my understanding, when I use a CNN on an image ...
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Problem with a feature (normal distribution + peak around 0)
I have a feature that shows a characteristic of the instances. That characteristic can be present or not. If present it shows an almost normal distribution of values (actually a bit skewed to the ...
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Dealing with highly variable feature set size
I'm trying to use machine learning for security event classification. My goal is to predict the outcome (true positive or false positive) of a specific event. An event has a set of variables in it, ...
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Handling highly correlated features [closed]
I have a data set of transactions and want to build a fraud detection model (classifier). Only 3 variables are given that could be used as input features. The number of transactions during past 3, 6 ...
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How to handle a valuable feature that is missing on 99\% of the samples in the data set?
Suppose we have an input feature that is highly predictive of the outcome we want to predict. However, the feature is missing on 99% of the samples in the data set. What is the best way to use this ...
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What's the difference between transfer learning and feature extraction in CNN?
So from what i understand, transfer learning is the fact of training a model on a dataset where you have a lot of data, then keeping most of trained coefficients, and only re-training the last layer ...
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Should I normalise image pixel wise for pretrained VGG16 model
My goal is to use pre-trained VGG16 to compute the feature vectors excluding the top layer. I want to compute embedding(no training involved) per image one by one rather than feeding batches to the ...
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Can machine learning models treat a vector as a whole feature to learn
We know a ML model naturally takes a feature vector with real valued elements as input and learn to predict. But can it treat a fixed-size vector as a whole feature to learn?
For example, when using a ...
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Feature Selection - Conditional Entropy
I've developed an algorithm to define conditional entropy for feature selection in text classification. I'm following the formula at Machine Learning from Text by Charu C. Aggarwal (5.2.2). The author ...
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Recommended Tutorial Videos or Books on Feature Engineering Using Python [duplicate]
I will appreciate it if you guys can recommend for me a good hands-on tutorial videos or books on feature engineering using Python. I do not want videos or books that teach only the theory behind ...
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What is the suggested way to create features (Mel-Spectograms) from speech signal for classification with ResNet?
At the moment I have this piece of code which cuts a Spectogram into fixed length tensors:
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How to utilize measurement accuracy metadata in classifier
Given that one wants to ascribe a class to groups of measurements using a classifier model, in what way can one include information about measurement accuracy?
More specifically, is there a feature ...
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How to extract audio features for each video frame using pyAudioAnalysis
I'm trying something like extracting audio features for each video frame. I know there are 30 video frames and 16000 audio frames per second in the video file. I'm using pyAudioAnalysis python lib to ...
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Build Deep Belief Autoencoder for Dimensionality Reduction
I'm working with a large dataset (about 50K observations x 11K features) and I'd like to reduce the dimensionality. This will eventually be used for multi-class classification, so I'd like to extract ...
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How to handle multi-channel 2-D geo-spatial grid like data samples in machine learning with number of features associated with each grid?
I am looking into a problem wherein the whole geographic area is divided into number of bins/pixels so we get nxn matrix covering whole region. Now each bin/pixel has number of parameters/features ...
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Is there a common relationship between data inputs and the number of attainable features?
Is there a known relationship between the amount of information gain that comes from new data added to a dataset?
for eg: If I have a plant watering system that tells me:
An integer of how wet the ...
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Autoencoder feature extraction plateau
I am working with a large dataset (approximately 55K observations x 11K features) and trying to perform dimensionality reduction to about 150 features. So far, I tried PCA, LDA, and autoencoder. The ...
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How can access to modify feature_importances of Random Forest Classifier model?
My goal is to extract the feature importances from already trained random forest classifier and transfer them to another classifier. How this can be done? and How can access to modify ...
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Single image feature reduction at inference time : SVM
I am trying to train a SVM classifier using scikit-learn.. At training time I want to reduce the feature vector dimension.
I have used PCA to reduce the dimension.
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How to combine the features extracted from different CNN architectures? [closed]
I have two CNN models, and I trained both of the models. The task is to extract the feature vectors from both models and combine them. How should we proceed?
CNN 1:
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How to access the data of the column on which some groupby operation has been carried out? [closed]
Suppose there is a pandas dataframe which has one column consisting of names of something, and there are multiple entries respective to each entry in the first column. To count the number of entries ...
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How do stacked CNN layers work?
The internet is full of pictures like this:
But how are the second/third/etc CNN layers able to extract features when the features are already extracted by the previous layers?
For example, the mid-...
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Dimensionality Reduction on Cross Channel EEG data
I'm working on a project predicting seizures events using 10 minute EEG data recordings. This involves identifying the preictal (pre-seizure) phase from the interictal (normal/between seizure) phase, ...
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File path encoding to feature
I am trying to find some sort of encoding algorithm that would allow to transform system file paths eg. "c:/users/file1/subfile2/targetfile" into a feature that I could use in machine ...
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What are some good methods to forecast future revenue on categorical and value based data?
I have monthly snapshots (3 years) of all the contract data. It includes following information:
Contract status [Categorical]: Proposed, tracked, submitted, won, lost, etc
Contract stages [...
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Is there any problem with dropping only part of the OneHot generated features?
The one hot encoder adds more columns to the data, one for each category in the encoded feature. In the example below, the column City was transformed into 4 other ...
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Finding a range of values for each feature that contribute to positive classification
Consider a classification problem(lets say 2 classes, 'good' and 'bad), where all features are continuous.
what I need is a range of values for each feature that contributes to 'good' classification....
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Importance of features
It is common to say in ML feature selection that features that are irrelevant in isolation can be important in combination with other features. Is there a simple example (one or two features) to ...
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Extract information using NLP and store it in csv file
I have a text file that stores the pickup, drops, and time. SMS text is a dummy file that is used to train a cab service model. The text is like in this format:
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