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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8 views

How to summarize/describe feature trends with fix number of scalars?

I'm trying to make feature extraction from features with numbers over different timespans (like stock values movement) and I want to encode these trends into a fix number of descriptive scalars. I ...
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1answer
2k views

How to extract features from the encoded layer of an autoencoder?

I have done some research on autoencoders, and I have come to understand that they can also be used for feature extraction (see this question on this site as an example). Most of the examples out ...
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Extracting Features for Graph transformation

Suppose I have a directed graph G (V,E) whose transformation is defined by a library of patterns. Each vertex is of particular type. The library of patterns contain subgraphs (g1,g2,g3 etc)and it's ...
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Is (manual) feature extraction outdated?

I recently attended a PhD thesis defence in which one committee members claimed that "manual feature extraction is outdated. Nowadays, we have [deep] machine learning models doing that job for us ...
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1answer
52 views

Information Gain & Gini Index for NLP

I know how Information Gain and Gini Index work in General. I have problem figuring out how to apply these techniques in NLP and text feature extraction. Can someone show me an example of how to ...
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1answer
86 views

What to do with large number of collinear variables?

I have this time-series dataset that has 63 features, out of which 57 were manually engineered. While checking for collinearity, I get this correlation matrix: As can be seen there are a number of ...
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37 views

Pipeline that cached the results

I use pandas to do feature extraction for machine learning. I hope to achieve the following: Consider I have five data processing steps done sequentially, and I execute it once, the results will be ...
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1answer
46 views

extract document topic vectors from lda model

how can I extract document-topic matrix from LDA model and use it as input features an svm classifier? I am using gensim for implementation
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Accelerometer and Gyroscope features

I am having accelerometer and gyroscope reading along x,y,z axis and want to get motion direction info at each time step. What all feature extraction would be best suited for this type of requirement. ...
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What techniques can I use to find position relationship in group of elements?

I have 14,000 tagged documents. These are custom forms that our employees create and fill out. I need to build a model that will be able to classify the types of each input field of the form in order ...
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17 views

What is the use of having shared weights in later layers of a CNN?

In a CNN, all the neurons in a single layer use the same weights and bias. As a result, all the neurons detect the same feature. The early layers of a CNN detect simple features like edges and hence ...
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31 views

feature extraction from single word for classification into nouns and names

I would like to write a NN that can classify different kinds of words(e.g. nouns,verbs,names) and am struggling to find information on how to do feature extraction on single words.For example, i would ...
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78 views

Feature extraction; similarity and classification of accelerometer data

I have several expert persons performing the same specific action (for example, squat or leap forward) multiple times. Say 5 persons do 100 squats each. They have an accelerometer attached to the same ...
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1answer
84 views

Feature selection is not that useful?

I've been doing a few DataScience competitions now, and i'm noticing something quite odd and frustrating to me. Why is it frustrating? Because , in theory, when you read about datascience it's all ...
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164 views

Intuition behind the PCA algorithm

I am trying to understand PCA intuitively. Here it goes: After finding the eigenvectors and eigenvalues of the covariance matrix of the dataset, the eigenvalues will represent how spread out the ...
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1answer
196 views

Bag-of-words model : Boolean vs. TF-IDF

When I design a document classifier using traditional feature engineering, I would prefer (to Boolean model) tf-idf model to represent a document into a vector because intuitively Boolean model loses ...
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Bayesian classification of “JSON” data

"Machine Learning over JSON" describes some issues surrounding the classification of JSON documents. Namely, Categorical Features Data is Hierarchical Missingness is Chunky The first two have fairly ...
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1answer
29 views

Adding time as a feature with xgboost/random forests

I am trying to use xgboost for performing some regression and the features I have are rather simple and limited. I have the time stamp associated with some ...
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How to use skimage.feature.hog to calculate hog of training data where examples are stacked vertically

I have a $n*1024$ dimensional $2D$ array where n is number of examples which contain n images($32*32$) stacked vertically. I would like to calculate the hog of these images ...
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1answer
199 views

Feature extraction in audio spectrogram

I have audio and its spectrogram of the words in English language. (A spectrogram is a frequency domain representation of a signal) Consider the words: chain, change, chair, chapter. As you can notice,...
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What is the dimension reduction method to large numbers of independent features while only two of them are important?why?

What is the dimension reduction method to model a data with large numbers of independent features (for instance 5k features), while only two of them are important (are effective in cost function)? I ...
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1answer
35 views

Modeling the Price Movement- What analysis should be used

I am trying to model the price of a hotel as the check-in date arrives. I have a data set which looks like- For e.g- if I am looking at the booking date of Dec 31st, I would want to analyze the ...
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1answer
412 views

Any issue with “overlapping” sliding windows in time-series data analysis?

I am developing some classification/regression models form accelerometry time-series data. So far, I have created datapoints by extracting features from non-overlapping sliding windows of the time-...
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2answers
106 views

Blind feature engineering

I received a dataset for analysis that had ~100 numeric columns with anonymous column names($X1$, $X2$, $X3$, etc...) and asked to do a binary classification. My resulting classification algorithm ...
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1answer
351 views

How and Why to rescale image range between [0,1] and [-1,1]

I am trying to implement model described in Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network in which author says in section 3.2 that We scaled the range of the ...
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1answer
54 views

Extracting information from online job postings

Okay, so I'm trying to build a data set about data science job openings. I want to extract information about what kind of minimum education level is expected in each job posting and also how much ...
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1answer
46 views

Dimensionality reduction based on value of a variable

I have a dataset including 100k high dimensional data (e.g. houses in LA) (dim=100, e.g. house parameters like area, distance to downtown, etc.). Below is the 2-component PCA representation of the ...
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4answers
1k views

how to avoid tokenizing w/ sklearn feature extraction

I'm trying to analyze some machine log files and the column I'm looking at can have values like 'Part.C1.11.Reading Status'. I want to treat the complete string as one token and I don't want it to be ...
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Confusion regarding prediction results of SVM and ANN on feature vectors

I am making a custom image classifier using Transfer Learning on Inception V3. I have 3 classes of images with ~6K images each. The input dimension of the network is 500X500 and the output of the ...
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1answer
1k views

Feature engineering for categorical variables

I have some categorical variables in my dataset for a regression problem. 1) One of the variable can take 3 values (Girls, Boys, Girls&Boys). Converting it into one-hot encoding or binary ...
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1answer
394 views

How to treat a categorical variable which can have multiple values in regression?

I am working on a regression problem and has a categorical feature 'CATEGORY' which can take as many as 1600 categories. On top of that, each observation can have any number of categories in that ...
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3answers
216 views

Why do RNNs usually have fewer hidden layers than CNNs?

CNNs can have hundreds of hidden layers and since they are often used with image data, having many layers captures more complexity. However, as far as I have seen, RNNs usually have few layers e.g. ...
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1answer
35 views

Potential problems with expanding training set

The problem is a binary classification one. My dataset contains users with activity over multiple days, where they all start with class 0 and can become class 1 after a certain activity (which is not ...
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1answer
4k views

Creating a Object Detection model from scratch using Keras

I have a dataset containing 330 images which contain guns. Along with the images, I have a text file associated with each image file which contains, The number of objects ( guns ) in the image. ...
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1answer
30 views

How can I train a many-to-one RNN with an array of 2D matrices?

I have eye tracking data for every word of a novel. Features for every word is given separately. I want to take groups of 100 words to make a sample and then use each of these samples as a single ...
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1answer
82 views

Feature Importance Scores Python

I have a dataset having 7 attributes viz., time, C1, ... C7 pertaining to earth quake reports where each column/attribute represents a certain aspect of damage viz., power, sewer_and_water, ...
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1answer
43 views

how to deal with two high correlations feature which both has a low correlation with target

I am doing a prediction of house trade money. Here is the correlation matrix of features whose correlations are larger than 0.3 as follows: ...
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1answer
137 views

Best way to classify plots which are overlapping?

I have an experiment in which it was done under two conditions. For each condition, the experiment was performed 26 times. The output of the experiment is a plot with 70 time indices. I would like to ...
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1answer
66 views

Can we use pca for supervise classification?

My questions are: Can we use "pca feature selection" for supervised classification? What will happen to labels when we use dimension reduction? If I understand it right when we use pca for feature ...
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1answer
37 views

Classification of data generated by radar using FFT

I have time domain data which is having binary label in form of 0 and 1. I applied FFT to all the features and label as well. The problem is my output label is now not binary anymore. How to solve ...
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1answer
59 views

What are the audio features to best describe a music?

I'm working on the content-based filtering part of a recommender system for an audio streaming project. I firstly used the k-mean algorithm with music genres and one-hot encoding to classify musics ...
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1answer
295 views

Feature importance after PCA (or other dimensionality reduction methods)

I have text data which I one hot encoded and then used PCA on it (although I'm experimenting with other methods as well, LDA, NMF..). I am using the result of the dimensionality reduction as an input ...
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1answer
208 views

Printing Feature Contributions in a Random Forest algorithm from the Treeinterpreter library leading to errors

I am working on a dataset where I predict the risks of developing pancreatic cancer with respect to a number of variables. I have created a random forest, and want to find the feature contributions. I ...
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1answer
172 views

BERT has a non deterministic behaviour

I am using the BERT implementation in https://github.com/google-research/bert for feature extracting and I have noticed a weird behaviour which I was not expecting: if I execute the program twice on ...
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20 views

How to incorporate an attribute that only exists in some observations?

In a binary classification problem, some of my observations have an event that occurs. I can, obviously, add a 1/0 flag if the event occurs ("event_occurred" in the data below). However, my intuition ...
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1answer
47 views

Machine learning algorithm that can use many instances to predict 1 continuous outcome per person

I am trying to use movements identified from accelerometers during sleep to predict gait speed (continuous). I am trying to figure out what the best machine learning algorithms/ feature extraction ...
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1answer
2k views

Calculating Feature Importance of Time Series Data

I am new to time-series modeling, and I was wondering what the standard way of quantifying feature importances are in a time-series setting? What types of models allow for the greatest interpretation ...
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3answers
417 views

How do I use TF*IDF scores for my machine learning model?

I have applied TF*IDF on the 'Ad-topic line' column of my dataset. For every ad-topic line, I get the same output: Firstly, I am unable to make sense of the output. The TF*IDF values are mentioned to ...
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1answer
331 views

How exactly do I extract the important features from strings for machine learning?

Forgive me for my ignorance. Linked below is an image of my dataset with 1000 tuples. https://i.stack.imgur.com/WHIlx.png I have the following questions (1) How exactly do I go about extracting ...
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575 views

How exactly do I go about extracting features from timestamps for machine learning? [closed]

My dataset has a timestamp column with the following format: 06/24/18 0:56 How exactly do I convert this information into features that can be used for classification algorithms like logistic ...