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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How to model Not Applicable (N/A) input in a Neural Network?

Assume I have a Neural Network with an input value to distinguish 2 discrete categories, e.g. Bicycle (input -0.9 in case of tanh) or Car (input 0.9 in case of ...
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12 views

How do I have select features which are influential for prediction?

I have a dataset which has dependent variable(label) as possible destinations and independent variable(features) as age,language, gender and many other categorical variables. How do i find which are ...
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9 views

How to implement Vectors in Feature Vector?

Please change the question title if you find a better one for my problem. I have taken some measurements with a 6 DOF (accelerometer and gyro) sensor. Now I want to use this data for a classifier ...
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8 views

Identifying important differences between supervised learning datasets

The training data in a multi-class supervised learning task shows a significant dependence on time that is apparently not captured well by my learners. Specifically, the two learners I used (OvR ...
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37 views

SVM accuracy after log transformation

I am using SVM (RBF kernel, the LibSVM implementation) to deal with a classification problem. When I use a log 10 transformation for may features values instead of using the default scaling method ...
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45 views

Is it better to do exploratory data analysis on the train dataset only?

I'm doing exploratory data analysis (EDA) on a dataset. Then I will select some features to predict a dependent variable. The question is: Should I do the EDA on my training dataset only? Or ...
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45 views

Using probabilities as predictor variables for binary classification

I have training data with each feature being different sources of probability. All of the features are probabilities (between 0 and 1 obviously). This is a binary classification problem. Note ...
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16 views

classification real vs. made-up words

I am interesting in building a classifier that can separates made-up words (such as brands) from real words (belonging to the English dictionary for example). I have tried using a Soundex ...
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11 views

Can we derive features from the output variable?

I know this sound weird, but can we use the output variable of the training data set to derive the some new features for feeding the model. If yes then how can it be statistically significant?
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1answer
65 views

Best approaches for feature engineering?

I have a regression problem. The aim is to estimate the best fitting curve from a set of features. Now I have extracted a set of features that are relevant based on the literatures found. Now the ...
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8 views

what is the method in dictionary learning which does not have a overcomplete dictionary?

what is the method in dictionary learning which does not have a overcomplete dictionary? and what is the difference in minimization between these two methods (one using overcompelte dictionary and ...
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18 views

Position-Invariant feature representation

If I have a protein multiple sequence alignment, in which the canonical protein length is 100 amino acids, I can transform this protein sequence into a sequence feature space by "binarizing" each ...
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8 views

MFCC and VQ - text independent speaker recognition

Im trying to create models for each person of my system to identify them. I did some preprocessing split sound into frames and extract mfcc. After feature extraction I get matrix ...
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21 views

providing MFCC as a feature to WEKA

I am beginner in WEKA, and for my first study, I am doing a music classification task. For that I need to use MFCC of a music file as a feature. When I extract MFCC, I get a csv file as given below. ...
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62 views

Denoising Autoencoder

I am trying to apply DAE to my task, but since I have experienced some problem with it, to get some feeling of DAE I tried first to try to follow the study performed in ...
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1answer
68 views

How to convert the text columns to libsvm format using feature hasher from scikit learn

This answer links to a code which only works for numeric data , but I have CSV file for machine learning which has mostly text data and most columns have a large cardinality , eg: a column with name ...
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40 views

Ensemble LDA on different feature spaces?

I'm working on a classification problem where I'd like to do the following: I have a space of features that live in $R^m$, and another set of features that are related that live in $R^n$. I want to ...
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14 views

CFA when there are no latent variables

I have reasonable results from a OLS regression with five observed variables. This is in a research article submitted for possible publication. One reviewer has asked for CFA (Confirmatory Factor ...
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1answer
58 views

Use features of auto encoder in SVM

Suppose I use an auto encoder for feature learning on a certain dataset. How can I use the learnt features e.g. for a classification task? Should I feed a SVM with the reconstructed output or with ...
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2answers
58 views

Regression: Difference between Instrumental variables and Feature Extraction?

I researched the topic on the internet and to the best of my knowledge there is no existing literature on the subject comparing and contrasting the pros and cons of the two methods. It seems that ...
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2answers
45 views

Advice on feature selection

I have a large universe of features, and potentially a large universe of targets that I want to use them for. I need to construct some kind of summary stats that ranks the features by their relevance ...
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8 views

Grouping erroneous factors into a single factor

For this specific machine learning problem, I have a data set with numerous features. I'm not too worried about the interpretability of my data set as the intention is to optimize prediction. I am ...
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2answers
36 views

Visualizing Nodes in a Neural Network (Dimensionality Reduction?)

Imagine we are working with the MNIST dataset and creating a neural network with 1 hidden layer. So we have a vector of 784 inputs, 100 hidden nodes, and 10 outputs. If we were to visualize each ...
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34 views

Dimensionality reduction of multiple signals using Fourier transform

I have $N$ recorded signals, $x$, each of which have been sampled 672 times across a time period of a week (= 15 min intervals). I will denote $x_{ij}$ as the $j$th sample for the $i$th recorded ...
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1answer
47 views

Combining several features into one single feature

I would like to know what might be the side effects of combining several features into one single feature for classification tasks. Imagine I have two variables with the following domains: A is ...
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1answer
36 views

Clustering data with Fourier series representation

We are analyzing temporal behavioral patterns across many users and we want to cluster users in order to understand "natural types of behavior". Our idea is to represent the data (672 bins for each ...
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1answer
54 views

Feature selection for sports prediction

I worked through couple of projects on Kaggle to pick up machine learning. Now I am looking to implement a basic tennis prediction algorithm. I have a very beginner question. Let's say, for each ...
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18 views

Dimensionality reduction for narrow, tall matrices

I have a matrix with three columns and a lot of rows. The first two columns contain integers N1 and N2. N1 is always {0,1,2,3}, but N2 can vary from let's say -50 to 50. It's always the same N1s, but ...
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48 views

How can I estimate the influence/significance of the every observation on classification?

There are many ways to estimate the significance of the features on the classification model. But how I can estimate the influence of the every observation on the classification quality? My thinking ...
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51 views

How to measure similarity between features in different datasets, if possible?

Is it possible to measure the similarity between two different features in different datasets? It could sound like a non-sense question, but it has sense in the context of merging two almost ...
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1answer
80 views

Random forest feature adjustment

so, I've found the misclassified instances in my Random forest model have lower values in some predictors, how can I adjust the model so that the threshold is more sensitive to these predictors? ...
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1answer
73 views

Feature that is numeric if true, or single value if false

In regards to feature engineering for machine learning models. I would like to engineer a feature that encodes the following: value can be true and if so it will measure a numeric (maybe ...
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21 views

What is the general procedure or general rules for grouping factor levels?

I am attempting to build a predictive (machine-learning) logistic regression model that contains mostly categorical (non-ordinal) variables. As part of a variable selection process I run a Pearson ...
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40 views

Neural Net in high dimensions for images

I'm trying to build a neural net for a image recognition problem. My images are way too large to build a straight up NN from just the pixels; they are about (1000, 1000) width,height. So naturally i'm ...
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12 views

How to analyse periodic data obtained from a vibration experiment?

I have data from a vibration experiment on a structure. This data was obtained by attaching an accelerometer to a FFT analyser, which records the data at a high sampling rate (typically in kHz range) ...
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12 views

Feature Normalization & Learning

I'm working on a cell classifier (as in Biological Cells) using images obtained by microscope. Right now I have about 12 Features written (color,width-height ratio, shape, couple of texture features, ...
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2answers
58 views

Why we need to extract a lot of features from a dataset for classification

I am newbie in machine learning. I have been studying about features extraction and some classification approaches, in the term of my study, I have a question in my mind, what the reasons we need to ...
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1answer
29 views

Storing a kernel matrix

I'm reading a paper on feature hashing and the authors state in the introduction that "limited memory makes storing a kernel matrix infeasible." I'm confused as to why the kernel matrix needs to be ...
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1answer
44 views

Representing signals as feature vectors for deviation detection

I want to monitor (automatic-)gearbox failures on some vehicles. For each vehicle I have a captured signal representing the selected gear at each one millisecond (the values are discrete between 0 and ...
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2answers
62 views

Weighting features prior to SVM

I'm building an object detector using HOG features and linear SVM. Some of the regions of the object are more "distinctive" so I would like to give more weight to the features extracted from those ...
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67 views

How can I extract features from fMRI network connectivity analysis (FSL nets)?

I have a set of 37 fMRI images from mice which are divided into 4 classes (different drug doses applied). My task is to train classifiers (SVM etc.) on this dataset. Of course feature extraction is a ...
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1answer
38 views

What to do with important features?

I am currently solving the titanic problem in kaggle. The data of the problem consists of several features such as "sex", "class in society", etc., and you are to predict whether a person survived the ...
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24 views

Binary Classification of Labels with Similar Feature Distributions

I wish to classify gene interactions as 'Validated' and 'Unvalidated' based on certain features of each interaction. Each interaction has 10 different features. However, the feature distributions of ...
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59 views

Does feature size affect polynomial regression?

(I'm still trying to learn all this, sorry for any wrong terms or mistakes I might have made in this question) By feature size, I mean the value of the numbers. For example, let's say I have input ...
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2answers
62 views

How well should features discriminate to build a good classifier from them?

For my (binary) classification problem I'm developing several features and tune them with ROC curves. At some point, I want to combine them with in classifier. How well should the features perform, ...
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114 views

kMeans unsupervised feature learning on multiple layers

I'm trying to develop an unsupervised feature learning pipeline. I have a train set with 512x512 images. I've extracted 16x16 patches, performed preprocessing steps (normalization and whitening). ...
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2answers
61 views

Should you create a word vector before cross validation?

We are doing a lot of experiments in my research group with text data, and what usually happens is that a corpus will be transformed into instances with features as bag of word or n-gram features. We ...
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28 views

What kind of feature selection do I need for text mining?

I have a data set of questions belonging to 10 different categories namely (definitions, factoids, abbreviations, fill in the blanks, verbs, numerals, dates, puzzle, etymology and category relation). ...
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1answer
25 views

How to compare utilization rates?

I have the utilization rates of several machines for each week over a year. These differ per week because of the occurrence of machine failures and changes of orders. Meaning that one week a machine ...