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.

Filter by
Sorted by
Tagged with
1
vote
0answers
16 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) ...
0
votes
1answer
24 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 ...
0
votes
0answers
20 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 ...
0
votes
0answers
11 views

Positional Encoding for 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 ...
0
votes
0answers
13 views

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 ...
0
votes
0answers
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 ...
1
vote
0answers
25 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 ...
1
vote
0answers
38 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 ...
1
vote
0answers
30 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 ...
0
votes
1answer
20 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 ...
1
vote
1answer
28 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. ...
0
votes
0answers
16 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 ...
1
vote
1answer
20 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 ...
0
votes
0answers
10 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 ...
0
votes
0answers
21 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 ...
1
vote
1answer
55 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)...
0
votes
0answers
29 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 ...
0
votes
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:...
0
votes
0answers
9 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 (...
1
vote
2answers
58 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. ...
0
votes
0answers
130 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 ...
2
votes
5answers
82 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 ...
2
votes
1answer
34 views

How to handle class imbalancing 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 which happens less often than other classes, just around 5% of all cases. To be precise, the ...
1
vote
0answers
29 views

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 ...
0
votes
0answers
19 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 ...
0
votes
0answers
15 views

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 ...
0
votes
0answers
24 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 ...
0
votes
1answer
35 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 ...
0
votes
2answers
87 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 ...
2
votes
2answers
65 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 ...
2
votes
0answers
39 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 ...
1
vote
1answer
50 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 ...
1
vote
1answer
27 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 ...
2
votes
1answer
45 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. ...
0
votes
0answers
38 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. ...
0
votes
1answer
44 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 ...
0
votes
0answers
15 views

Low Signal-To-Noise Ratio Data Processing and Model Choices

Attached is a plot showing that y regresses on individual features: Scatter Plot And as shown, each feature has minuscule predictive power on y. I have 130 features like this, all of them are more or ...
3
votes
2answers
50 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 ...
1
vote
1answer
75 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 ...
2
votes
1answer
140 views

In OCR, how should I deal with the warped text on the sides of oval objects?

Consider an image that contains one can (or bottle, or any similar oval object), which has texts all over it. In the image below, I have many bottles, but you can assume that each image only contains ...
2
votes
1answer
91 views

How robust are deep networks to class imbalance?

Before deep learning, I worked with machine learning problems where the data had a large class imbalance (30:1 or worse ratios). At that time, all the classifiers struggled, even after under-sampling ...
0
votes
0answers
22 views

Is it possible to automatically remove ignore (or remove) the equations (and other noisy elements) while performing OCR?

I have academic pdf data. I am using OCR for converting it into text format. The pdf has a few mathematical equations and terms which are acting as noise for my task. Any way through which the task of ...
1
vote
1answer
141 views

How to fill NaNs in Cross-Validation?

I have been searching this but did not find the answer, so sorry if this is a duplicated question. I was working with cross-validation, where some doubts came to my mind, and I am not sure which is ...
2
votes
0answers
61 views

Statistical method for selecting features for classification

I'm working on a classifier for the famous MNIST handwritten data set. I want to create a few features on my own, and I want to be able to estimate which feature might perform better before actually ...
0
votes
0answers
31 views

How to generate a matrix out of sparse data?

I have a system that takes 32 inputs (all of which are 1 or 0) and generates 32 outputs (all of which are complex numbers that lie roughly in the range of (0,2)). The response of this system to its ...
1
vote
1answer
37 views

Should binary feature be in one or two columns in deep neural networks?

Let's assume I have a simple feedforward neural network whose input contains binary 0/1 features and output is also binary two classes. Is it better, worse, or maybe totally indifferent, for every ...
1
vote
1answer
211 views

Why do we resize images before using them for object detection?

In object detection, we can resize images by keeping the ratio the same as the original image, which is often known as "letterbox" resize. My questions are Why do we need to resize images? ...
0
votes
0answers
35 views

Transforming neural network target values before training

Consider the scenario in which I am measuring certain $f(a,x)$, which i want to be the target value for some related input $g(a,x)$. In other words, I am trying to map $$g(a,x)\Rightarrow f(a,x)$$ I ...
1
vote
1answer
127 views

How to mathematically describe the convolution operation (with a Gaussian kernel)?

I have to build a model where I pre-process the data with a Gaussian kernel. The data are an $n\times n$ matrix (i.e one channel), but not an image, thus I can't refer to this matrix as an image and ...
0
votes
2answers
53 views

What would be a typical pre-processing and data normalization pipeline for time series data (for non-linear models such as neural networks)?

I've started to work on time series. I was wondering what would be the best data normalizing and pre-processing technique for non-linear models, specifically, neural networks. One I can think of is ...