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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27 views

Different extraction pipeline for train and test

I'm trying to create a production-ready ML model. The problem is as follows: Training data: Database A + Plus python aggregations. Testing data: Database B + Plus python aggregations. Both ...
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15 views

Extract specific Information from unstructured Documents

I am trying to build a system where recruiter will upload a doc file with Job Roles , Location , Experience , Title . the problem is every user will upload a different format document. Please Visit ...
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1answer
54 views

Find the top n features from feature set using absolute values of `feature_log_prob_ ` parameter of `MultinomialNB`

I am working on Donors choose dataset and have converted categorical, numerical and text features into vectors. I want to find the top 20 features from my 5095 features using absolute values of ...
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30 views

how to determine percentage below which a feature can be removed from a model

Let feature $feat$ contain one value $A$ that occurs 5% of the time, while 95% of the time it is empty. Instead of arbitrary saying features that have less than 5% should not be included into the ...
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52 views

How to get significance level for ranked features?

I am aware of below approaches of feature selection a) Feature Importance methods which are available in tree based models like Random Forest and ...
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45 views

What does many low important feature indicate?

I have a dataset where I am focusing on binary classification problem. In total,I have around 60 features in my dataset When I used Xgboost Feature Importance, I ...
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24 views

How to extract crucial features to create an image

Imagine, you have a dataset containing pictures of (example only, just to explain the task) cats and dogs. The data set is labeled, so we can train using supervised learning algorithms. My goal is to ...
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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
80 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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28 views

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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2k views

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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18 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
38 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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6 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
29 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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11 views

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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7 views

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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11 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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1answer
22 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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2answers
67 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
62 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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25 views

Applying the fourier transform

I have been trying to apply Fourier transform for two days, but haven't been able to because all the examples I've seen so far are not on actual dataset and use standard values that I don't understand ...
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1answer
48 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
68 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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9 views

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
21 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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16 views

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
62 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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25 views

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
29 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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15 views

What are the common feature extraction technic to compare 2 sequence of timestamps?

I am building some predictive models for an online shopping site. I have timestamp log of customers' arrival time, time spent on a product's webpage, purchased or not, and a few others. I have tried ...
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24 views

What is the meaning of constraint two neural networks against each other?

I have two identical autoencoders with the same number of layers and parameters. I would like to find the similarity between two images from different domains such as an image captured from the camera ...
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1answer
159 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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1answer
53 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 using a ...
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46 views

How to time series forecast with multiple time series data sets on the same time series index

How does time series work with multiple time series data sets on the same index? For example, suppose I were a utilities company. Suppose I have the electricity usage of two homes, each indexed for ...
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1answer
62 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
30 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
39 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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3answers
467 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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11 views

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
509 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
192 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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189 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
33 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
2k 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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48 views

Actually using scipy.signal.stft multidimensional array as a feature

I'm trying to improve my score in the LANL Earthquake Prediction challenge on Kaggle extracting more features from the acoustic data through STFT transforming. Eventually I've found this well written ...
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1answer
25 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
61 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
34 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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42 views

fourier transform on classification data

I have a dataset with sequences and date of occurrence of each element in the sequence. I wanted to extract some features from the date and I was wondering if its possible to use fourier ...