Methods and principles of selecting a subset of attributes for use in further modelling

learn more… | top users | synonyms (2)

1
vote
0answers
24 views

giving some predictors higher priority in statistical models

i am working on statistical software debugging. i use various feature selection algorithms in order to discover the bug predictors in programs. for example regression coefficients indicate the ...
2
votes
1answer
103 views

Features that correspond to rare events: how rare is “too rare” to be informative?

I am working with 82 binary features constructed from six categorical features. I have about 1,600 observations. Some of these features correspond to extremely rare categories. Some of them have only ...
0
votes
0answers
17 views

Tree analysis - CHAID cart

I am new to CHAID and want to know how to decide which independent variables I should select to run CHAID Analyis? Is there a technique to select and then apply them and run the analysis? Please guide ...
0
votes
0answers
22 views

In intuitive explanation how does GLAM (Gray Level Aura Matrix) works? [on hold]

and i look for a simple example about GLAM (Gray Level Aura Matrix).i need your help.
1
vote
1answer
29 views

Can adding an additional feature to a perceptron classifier make the results worse?

I am using perceptron to solve a classification problem. I have a limited amount of features (26) and iterate through all possible combinations of them. A combination of two features [feature_a, ...
0
votes
0answers
21 views

What will be the simple interpretation for the coefficients for features obtained in any Machine learning models?

I am working with a data that consists of two classes. I have used scikit learn, to craete models using SVM, Randomforest etc.I used to r2_score and I sorted the scores for features I am having and I ...
0
votes
0answers
8 views
2
votes
0answers
34 views

How to make use of less data of a particular class for better modeling?

I have a dataset, say 9000 rows, with some features. Around 8000 belong to class 1 and 1000 to class 0. So, if I am creating a model with any method say SVM, LR, Random forest the model has a tendency ...
0
votes
0answers
22 views

Feature Representation for Samples with Different Number of Properties

I want to build a machine-learning classification model that learn from properties (features) extracted from proteins. I represent each sample (protein) using some features (e.g. 100 features ...
1
vote
1answer
46 views

Scale-invariant feature transform explanation

How do I explain the scale-invariant feature transform (SIFT) to a layman?
0
votes
0answers
18 views

I observed very different feature scoring from two different classifiers. What does it really mean?

Here what I've done. Given the dataset, I run a Random Forests and Logistic Regression with 5 Fold Stratified Data Sampling. Then I plot the feature importance for Random Forests and Logistic ...
2
votes
1answer
31 views

Automatically fixing ill-conditioning or collinearity

I'm backtesting a regression model, which entails running it on a bunch of bootstrap samples of a "rewound" version of our data set. Unfortunately, in some of these resamplings, I end up getting some ...
0
votes
0answers
12 views

Classifying Signal Curves [closed]

I am interested in knowing some literature references regarding the following scenario: Given a set of signals, i.e., functions: $\mathbb{R} \rightarrow \mathbb{R}$, what are common approaches for ...
0
votes
0answers
20 views

Appending additional data to learnt autoencoder features

My task is to perform image segmentation / full scene image parsing. I am working on an outdoor dataset which was taken under strict spatial constraints. The images contain fruit on trees and the ...
-1
votes
0answers
22 views

Clustering binary letters/text - image features and descriptors

I have a task to cluster 1 000 000 images (where each image represents one letter). They are all in binary format. I need to get clusters where each of them would represent 1 letter.. Which image ...
1
vote
1answer
58 views

No significant tests when using Benjamini-Yekutieli multiple testing correction on millions of tests

I am using a univariate filter to reduce the number of features prior to applying a learning algorithm to a huge binary classification dataset (22510066 features x 500 examples). All the features are ...
2
votes
0answers
49 views

Deep learning: representation learning or classification?

For classification, I have often heard about deep learning / deep neural networks as a form of representation learning. I am confused as to what "representation learning" means in this context. Which ...
2
votes
0answers
47 views

Reference for this claim: important features in data can be “hidden” in the higher PCA axes that are typically thrown out [duplicate]

I remember reading a paper a while ago that demonstrated some cases in which PCA would fail to capture important features of a data set in the first few principal components, but where those features ...
0
votes
0answers
6 views

How can be assesed that a given data representation is better than the other?

Given a classification dataset, suppose I learn many different data representation with Matrix Factorization, Clustering or with such approaches. At the end , how would I decide which is better than ...
0
votes
0answers
22 views

Data-driven, high-dimensional feature selection strategies

I am working on a biomedical/healthcare data science problem. I have a dataset of 600 samples, ~6000 variables and class label as "positive" or "negative". I want to perform feature selection on ...
0
votes
1answer
24 views

Classifier with variable number of features

I am trying to make a classifier when each sample has a variable number of features. An example of how this could occur is, for example, if the features are the purchases (type, dollar amount, etc) ...
1
vote
2answers
59 views

How To Better Represent A Problem To A Machine Learning Algorithm

I am familiar with the basics of how to present a problem to a machine learning algorithm using binary encodings. I am also familiar with, but still learning about, feature selection/extraction and ...
0
votes
0answers
4 views

Find the relative importance of features weights in multi-class SVM without PCA - plotting coef distribution?

I'm classifing users with a multiclass svm (one-against-on), 3 classes. In binary, I would be able to plot the distribution of the weight of each feature in the hyperplan equation for different ...
0
votes
0answers
13 views

feature slection in random forest in python

I have a dataset consisting of 24 numeric features and about 7000 rows, i am applying random forest to get the binary classification, So please tell me how to find only the relevant features to get ...
0
votes
0answers
11 views

What's the probability that there exists a hyperplane that can split a dataset which have random feature values ?

Given n data points, each with d features, n/2 are labeled as -1, the other n/2 are labeled as 1. Each feature takes a value from [0,1] randomly (uniform distribution). What's the probability that ...
2
votes
1answer
35 views

Will adding additional features hurt the performance of SVM ?

Just wondering the effects of additional features. Following are several thoughts: If the additional features are noisy (can not distinguish the two classes), then additional features won't hurt ...
1
vote
1answer
14 views

What kind of general strategy can you apply after selecting model and hyper parameter training?

As a rookie to machine learning area, I tried to play some Data Science tutorials and beginner competitions to gain some knowledge and experience. The problem I encountered in every scenarios is ...
1
vote
0answers
13 views

Relation between chi-squared statistic scores and classification accuracy

I am evaluating the utility of two distinct sets of features for solving a given supervised classification problem with two classes. I am using the chi-squared statistic as a feature selection ...
0
votes
0answers
9 views

Finding the features that have meaning in subset of data

I have a set of $N$ points $x_i=(x_i^1, x_i^2,...,x_i^{m+k})$ in $m+k$-dimensional space ($m$ continuous dimensions and $k$ discrete). Also I have a subset of these points that are marked as "bad". ...
0
votes
1answer
25 views

After adding additional features, same accuracy on test data, but higher accuracy on training data, how should I interpret ?

I've done 5-fold cross-validation and the model is SVM. 300 features: 0.53 on test, 0.55 on training; 700 featuers: 0.53 on test, 0.67 on training. Does this mean that the additional 400 features ...
1
vote
1answer
52 views

Feature selection for time series data

I am looking for methods for feature selection (or feature extraction) for time series data. Of course I did some research before, but it was not satisfying. I am aware of methods like PCA, ...
1
vote
0answers
25 views

High dimensional explanatory variable

I have a data set of 22 observations and 6931 variables. Data belongs to two classes, 0 and 1. I would like to know which features are important for each class (species) and which one contribute the ...
1
vote
0answers
21 views

Learning if instances from a dataset are part of the same subset

I was wondering if there are some well-known machine learning methodologies for subset learning. In other words, to learn if two instances are part of the same subset or not (boolean label?). One ...
2
votes
0answers
43 views

How to increase the performance of random forest classifier?

I have a text classification task. These are the metrics for different languages at present: class1: 0.6823 class2: 0.7450 class3: 0.66 class4: 0.6719 How can I ...
0
votes
1answer
103 views

R gbm package variable influence

I'm using the excellent gbm package in R to do multinomial classification, and my question is about feature selection. After deciding the number of iterations ...
0
votes
0answers
17 views

Feature Selection for Regression Models in R [duplicate]

I’m trying to find a feature selection package in R that can be used for regression. Most of the packages implement their methods for classification using a factor ...
0
votes
1answer
44 views

Feature selection in GBM

I am using gradient boosting (caret package in R). As far as I understand, the feature selection is already included in this package. However, I slightly ...
8
votes
4answers
188 views

How can top $k$ principal components retain the predictive power on a dependent variable?

Suppose I am running a regression $Y \sim X$. Why by selecting top $k$ principle components of $X$, does the model retain its predictive power on $Y$? I understand that from ...
1
vote
1answer
81 views

GBM: Predict the response variable measured in {0,20}

I need to predict the response that has values in {0,20}. Should it be used as a factor or as a numeric value? How does it influence on the prediction error? I am using GBM with the Gaussian ...
0
votes
0answers
10 views

Need a statistic for comparing “strength” of Markov blankets in a Bayesian network

Working with Bayesian networks. I take a given network structure and fit its parameters on data. I am looking for a statistic based on those parameter estimates that allows me to compare Markov ...
0
votes
0answers
27 views

How many samples are enough

I have objects with large number of attributes (about 60.000). Attributes are actually deviations of object part from model. I would like to cluster this objects, to get centroids that will represent ...
0
votes
1answer
30 views

Find entropy in WEKA

I am new in data mining so sorry for asking this kind of silly question. I am working on FAST feature selection algorithm and for that I need to find entropy of each attribute in dataset. But the ...
1
vote
0answers
17 views

feature selection in a small sample size

I need an advice. I have a dataset consisting of 108 observations (27 subjects * 4 time points) and ~10000 features. The data represents intensity values (comes from continuous domain). When I run ...
6
votes
4answers
139 views

How to prepare/construct features for anomaly detection (network security data)

My goal is to analyse network logs (e.g., Apache, syslog, Active Directory security audit and so on) using clustering / anomaly detection for intrusion detection purposes. From the logs I have a lot ...
3
votes
2answers
100 views

Appropriately selecting explanatory (independent) variables

My aim is to carry out a GLM. I have 400 sites where I have count data of animals (response variable) and environmental characteristics (explanatory variables). At the moment I have around 40 ...
0
votes
0answers
15 views

What must be the sample size for feature selection by coefficient correlation method?

I have eight features from which I want to select 2-3 significant features for classification. The method which I adopted for doing so is coefficient correlation. The problem I am facing is for ...
1
vote
0answers
12 views

Will the classification accuracy vary if we first classify based on a single variable and then use the rest?

Let's suppose I am doing classification and that I have 99 features and another feature that says if the person is male or female. I have two options viz to build one classifier using all the ...
4
votes
1answer
69 views

What are the disadvantages of using Lasso for feature selection?

As far as I understand, feature selection is difficult for classification problems because it's effectively impossible to identify an optimal subset of $k$ features in problems where the the total ...
1
vote
2answers
90 views

Feature selection before neural network classification

I have a training set of 87 samples and 9480 variables. My predictors are continuous and my response variable is binary. I'd like to use the caret package in R to tune a neural network classification ...
0
votes
2answers
46 views

Which variables to keep in my analysis based on loadings from PCA? [duplicate]

Could someone please explain me how I should decide which variables to keep in my analysis based on loadings from PCA. The output is: ...