Questions tagged [data-preprocessing]

For questions related to the concept of data pre-processing, which includes, for example, cleaning, instance selection, normalization, transformation, feature extraction or selection.

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11 views

How to use tabular data and figures when the structure of data varies?

Suppose I have several journal articles. I would like to train a binary classifier on whether the journal article is insightful. NLP models such as BERT certainly fit my need by scanning the whole ...
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23 views

Transforming Moving Position Data to an Inputvector for Neural Networks

Imagine a car is driving on a long street (= x-axis). The car can go in both directions and it will arbitrarily change its direction. I'm trying to formulate an Inputvector to tell a neural Network ...
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How to represent billions of time series data? [closed]

I have a time series (e.g., temperature data) from 1st January 2003 to 31st December 2017 with a one-second sampling rate, which indicates there are about $24 \times 3,600 \times 365 \times15= 473,040,...
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63 views

Which pre-processing steps are necessary for Deep Learning models to solve a document classification problem?

I have created a data set with 30.000 text documents (each text file is rather small with respect to its length), which are labelled with 0 and 1. Using this data set, I want to train machine learning ...
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preprocessing of time series data, each line consist of a time series

Imagine having a dataset (almost 100000 observations) composed of 365 columns (1 year) and each index (or observation) will then be representing time-series data. In my case, each observation (time-...
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6 views

Predicting single floats based on set of 2 feature arrays each of 100 values

I am trying to predict audio to video desynchronization based on ser od two arrays of lenght 100 which consist of coresponding audio and video samples. The problem is that my labels are single floats (...
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1answer
38 views

Are derived or computed inputs bad for CNNs?

I am building a CNN and am wondering if inputting derived or computed inputs are generally bad for the effectiveness of CNNs? Or just NNs in general? By derived or computed values I mean data that is ...
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1answer
36 views

General approaches in text encoding and labelling for NLP [closed]

What are the approaches of encoding text data? I would be glad to hear some summarization from experienced persons. And are there any solutions accepting words outside the vocabulary and including ...
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38 views

Can I flip a video to generate more data for action recognition?

There are 8 distinct action classes and around 50+ videos per class. I was wondering if flipping videos from the training set can be a good option to generate additional data. Is it?
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29 views

How to arrange test dataset distribution for an imbalanced classification problem?

I have a dataset that contains 560 datapoints, and I would like to do binary classification on it. 400 datapoints belong to class 1, and 160 points belong to class 2. In the case of an imbalanced ...
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1answer
30 views

Best practice for handling letterboxed images for non fully-convolutional deep learning networks?

I'm working on a depth estimation network. It has two outputs: A relative depth map A scalar for scaling the relative depth map into an absolute depth map. This second output uses dense layers so ...
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17 views

What is the sensible amount of augmentation?

I am playing with the transforms from Torchvision. There are plenty of different kinds of these like: Resize RandomCrop ...
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20 views

Does the converted (now square) distorted image of a face affect the accuracy of the calculation of the similarity in FaceNet?

As far as I know, FaceNet requires a square image as an input. MTCNN can detect and crop the original image as a square, but distortion occurs. Is it okay to feed the converted (now square) ...
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28 views

Given the immaturity of NLP tools for non-English languages, should I first translate the non-English language to English before text pre-processing?

For non-English languages (in my case Portuguese), what is the best approach? Should I use the not-so-complete tools in my language, or should I translate the text to English, and after using the ...
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27 views

Correct way for validation split in semi-supervised learning

I have a fully labeled Human Activity Recognition dataset from 9 participants. I am using one participant for the test set, and for the training, I use one participant as a labeled set and the rest as ...
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16 views

What are the pros and cons of using a normal positional encoding in an adjacency matrix?

I understand that a normal positional encoding helps a transformer to understand pictures better and that it allows the (otherwise permutational invariant transformer-network) to create relationships ...
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What is an efficient way to implement an contextual search for a search engine?

So I am working on a search engine and want to implement a contextual search for my search engine but would like to get some advice before I start working on it. I have a basic knowledge of A.I. like ...
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11 views

How reasonable is it to predict on a variable log scale?

Hi I know this is a super common practice and it seems reasonable to me. But I've actually encountered a problem recently where the benchmark MAE to beat was in exponential domain and naturally a fair ...
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26 views

Recommended way to spilt image sequence for training/validation/testing

For object detection tasks I have a few minutes of video footage from a surveillance camera, converted to a sequence of images and ground truth bounding boxes for all people walking by. Now what's the ...
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41 views

Is having near-duplicates in a training dataset a bad thing?

I am making a labeled dataset of images from web streams for a CNN classification. Pictures from the same stream are quite similar as far as background, but slightly different as far as the main ...
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33 views

Is it true that real world data is highly discontinuous?

A function $f$ is said to be continuous at a point $c$ if it satisfies three properties: Should be defined at the point $c$ Left and right-hand limits at $c$ must be equal i.e., the limit must exist ...
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1answer
24 views

Data analysis before feeding to ML pipeline

I'm new to machine learning and I've been working through a dataset of ~3000 records with ~100 features. I've been hand rolling Python and R scripts to analyse the data. For example, plotting the ...
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1answer
29 views

How can I address missing values for LSTM?

I'm a student and writing my first paper for submission on conference. I have a question there is a dataset below. this is temporal-spatial dataset. ...
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17 views

How should an ML model architecture be designed for predicting the order of a sequence?

I've decided to create a model for predicting Formula 1 race results based on driver statistics, to try to improve my ML skills. The first problem I've encountered is the data type of the target ...
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1answer
22 views

What to do when you have massive amount of data but you don't have enough computation power for training a machine learning model?

For example, I have a massive amount of data, but I have limited computational resources and time to train on the full data. Other cases may include, I have huge amounts of 360-degree images, where I ...
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11 views

Should we create a label mean group of small nearby objects in object detection?

I'm working on object detection models and my dataset sometimes has a lot of small objects (stay far from the scene) (overlapping and nearby) which is really annoying in annotating (it's too small and ...
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23 views

What is a good approach to apply smoothing techniques to time series with big changes and seasonality?

As far as I understand, it is always a good idea to apply a smoothing technique to a raw time series before training a model with it. However, I have a time series with big changes in magnitude and ...
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1answer
85 views

Rescaling time-series data with very spiky pattern for training data in LSTM network

I am working with some time-series hydrology data. Our goal is to forecast the time series forward, meaning predicting the data 1 month, 3 months ,6 months into the future. The data itself(image below)...
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31 views

Do the training and test datasets need to be equally preprocessed as one whole dataset?

I have developed, trained and tested an NLP model. It is persisted in a pickle file. The model contains the data preprocessing function that includes text cleaning and new features engineered with ...
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1answer
28 views

Normalization of possibly not fully representative data

I am trying to train a classification RNN model on a sequence of table medical data, but I stuck with the normalization problem. I realized that I cannot simply use MinMaxScaler, because of 3 problems:...
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12 views

Preprocessing deterministic data with sklearn

I am trying to create a set of ML models that will serve as a replacement for a complex deterministic simulation. The simulation requires 4 inputs (x1, x2, x3 and x4) to determine 4 different outputs (...
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2answers
66 views

Is creating dataset only by augmentation a bad practice?

I wonder if creating data set only by augmentation base images is a bad practice. I mean the situation when you have to train net to predict really simple patterns, for example printed-like digits. ...
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158 views

How does pairwise comparison training work in XGBoost's XGBRanker?

I'm interested in learning to rank with pairwise comparison. While working on this, I found that XGBoost has a model called XGBRanker, which works very well. I want to find out how the XGBRanker ...
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5answers
94 views

How do I increase the size of an (almost) balanced dataset?

I am trying to add more data points in my (almost) balanced dataset for training my neural network. I have come across techniques such as SMOTE or Random Over Sampling, but they work best for ...
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1answer
47 views

How to handle class imbalance when the actual data are that way

My supervised learning training data are obtained from actual data; and in real cases, there's one class that happens less often than other classes, just around 5% of all cases. To be precise, the ...
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Why do you calculate the mean and standard deviation over the complete dataset before training rather than for every batch?

In most implementations of neural networks the features are scaled to make the optimization of the loss function as stable as possible. Mostly a min-max scaler is used. Alternatively, there is also a ...
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20 views

Can I take some conclusion from two dimension PCA

I have a table of 140 features and try to predict binary classification. I create two dimensions out of this for visualization with PCA. In this visualization I can see, that data is not separable but ...
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Dealing with huge peak in data distribution

I am trying to predict a continuous value using a deep neural network. I have about 100,000 samples, where input is a sequence of RNA, and output is a continuous metric determining the quality of the ...
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32 views

Why (not) using pre-processing before using Transformer models?

Regarding the use of pre-processing techniques before using Transformers models, I read this post that apparently says that these measures are not so necessary nor interfere so much in the final ...
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1answer
37 views

How should I incorporate numerical and categorical data as part of the inputs to the U-net for semantic segmentation?

I am using a U-Net to segment cancer cells in images of patients' arms. I would like to add patient data to it in order to see if it is possible to enhance the segmentation (patient data comes in the ...
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2answers
94 views

Is my approach to building an RNN to predict the probability that the word is in English appropriate?

Goal To build an RNN which would receive a word as an input, and output the probability that the word is in English (or at least would be English sounding). Example ...
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2answers
75 views

Is feature engineer an important step for a deep learning approach?

I'd like to ask you if feature engineering is an important step for a deep learning approach. By feature engineering I mean some advanced preprocessing steps, such as looking at histogram ...
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48 views

How to deal with a variable number of channels of the inputs?

I have a problem in which my input data may have a varying number of channels. Let me explain with an example. Imagine we have a classification problem in which we wish to identify if certain species ...
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1answer
57 views

How to normalize images before training?

I have seen people normalize images by just dividing 255. But why? Why not use mean normalization or Z-score Normalization? I also came across this StackOverflow topic while searching but the answers ...
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1answer
31 views

Does feature scaling have any benefits if all features are on the same scale?

By scaling features, we can prevent one feature from dominating the decisions of a model. For example, say heights (cm), and age (years) are two features in my data. Since range of heights is larger ...
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1answer
46 views

Is data leakage relevant when scaling across samples?

I have a question about data leakage when pre-processing data for a neural network and whether data leakage actually applies in my instance. I have variance stabilising transformed genomic data. ...
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39 views

Data Augmentation of store images using handwritten labels

I am new to AI and NN. I've started learning using Geron's book on Tensorflow. My first project ("Smart Shelf") is to determine which items in a store have been purchased and need refilled. ...
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49 views

Denoising Images When Training a Classification Model

Suppose you have a binary outcome variable and have some training data (10,000 images in jpg format). Also you have a test set of say 11,000 images. If we want to train a classification model and want ...
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75 views

Is pre-processing used in deep learning?

I'm new to deep learning. I wanted to know: do we use pre-processing in deep learning? Or it is only used in machine learning. I searched for it and its methods on the internet, but I didn't find a ...
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77 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 ...