0
votes
0answers
19 views

Determine which variable or variables is/are the most efficient to predict the outcome

I have a small dataset (n=74) with a +/- 50 variables, not the best data but I have to work with it. The variables are used to select a product. I want to determine which variable or variables is/are ...
0
votes
0answers
45 views

What method should I use for this optimization / feature selection project

I'm going to describe a problem and I'm not sure how to best solve it. I will describe the situation. When answering please recommend a method and maybe a software library. I'm using Python for my ...
0
votes
0answers
54 views

How to prove the significance of features in classification?

I have a binary classification problem. I have extracted 500 features from a set of 5000 samples using my domain knowledge. In other words, I have got hand crafted features. I wish to prove that ...
1
vote
0answers
72 views

Feature extraction for customer churn data

I have a customer churn data, and would be implementing algorithms (decision tree, logistic regression, segment analysis). I have doubt on feature extraction procedure though. The training sample has ...
0
votes
0answers
19 views

Replacing categorical variables with historic response rate

In Linoff and Berry's "Data Mining Techniques" they mention reducing the number of categorical variables in a classification model by replacing the variable with the historic response rate. "When ...
2
votes
1answer
103 views

Are there any techniques that quantify the importance/signification of individual attribute values of a particular data point?

Are there any techniques that quantize the importance of individual attribute values in a particular data point, in terms of the attribute's overall importance/signification/contribution to the ...
0
votes
3answers
93 views

Classification performance and the feature set selection

I am now working on a classification problem. The generated feature set can be separated into two group. I did a comparison study: use all of the features; use the features of group 1 only; and use ...
4
votes
4answers
2k views

Term frequency/inverse document frequency (TF/IDF): weighting

I've got a dataset which represents 1000 documents and all the words that appear in it. So the rows represent the documents and the columns represent the words. So for example, the value in cell ...
0
votes
1answer
166 views

Feature selection and cross validation

I'm working on a project and I would like to know if the following strategy is good/correct. Sorry if this is a basic/stupid idea (I'm new to this). The input is a dataset with 2.500 features and ...
1
vote
0answers
76 views

How to explain difference of importance between feature selection and model quality?

I have a data collection with a mixed feature set consisting of both numerical features and text features. The number of numerical features is quite small, i.e., 6, comparing to the number of text ...
2
votes
1answer
69 views

Distinguishing two datasets

I have two datasets from some Web store (like Amazon). Datasets have one and the same structure. Each record in these datasets has the following attributes: ...
3
votes
0answers
84 views

Mutual information/pointwise mutual information for measuring prediction

I want to measure how well I predict a vector $Y$ (vector not a label) for observation $X$. Both $X$ and $Y$ have the same set of features ($1\times n$). For that, I thought of "scoring" the ...
1
vote
0answers
145 views

Features selection by filter methods for multivariate time series

I have a data set in which the samples are multivariate (about 30 variable/features) time series. These samples refer to two classes. I would like to select the variables more relevant to discriminate ...
1
vote
0answers
183 views

Event Prediction through Machine Learning

I have a large data set consisting of ca. 40 categorical data items and a few interval data items (real numbers, less than 5 such items). Most categories should have a lot of values that repeat ...
3
votes
1answer
60 views

Methods for teasing apart the influence of different time series features on a target feature?

Are there any established methods for teasing apart the influence of different time series features on a target feature? To illustrate: The target: Sales volume of kittens. Features: Time of year, ...
0
votes
0answers
178 views

What are the good algorithms for feature extraction for large dataset?

I have KDD dataset for detecting fraud actions on networks but it has millions of lines and >20 feature columns. Thus it is not viable to process all these on my personal computer. I am thinking about ...
3
votes
2answers
297 views

How to do feature selection for learning from positive and unlabeled examples?

I have a binary classification task for German webpages for which I only have positive examples. That is why I use learning from positive and unlabeled examples as described on this page, also known ...
5
votes
3answers
1k views

How to avoid overfitting when using crossvalidation within Genetic Algorithms

This is a long set-up, but the pure intellectual challenge will make it worthwhile I promise ;-) I have marketing data where there is a treatment and a control (i.e a customer gets no treatment). The ...
2
votes
1answer
343 views

Are randomForest variable importance values comparable across same variables on different dates?

Are randomForest variable importance comparable across same variables on different dates? I have a data array X which is of size $T\times N\times K$, where $T=1500$, $N=1500$ and $K=10$. ...
2
votes
2answers
435 views

Variable selection in large datasets

I'm looking for an overview of some methods of variable selection. I use datasets with around 6000 variables (the level of missing values is satisfying i.e. there are no variables with 100% missing ...
3
votes
1answer
715 views

Text feature vector extraction

I have a class assignment to implement a couple existing ways to extract feature vectors from a given set of texts, so they can be used to classify those texts using k-nearest neighbour algorithm. The ...
1
vote
0answers
149 views

Measures of predictive power of attributes in data mining

What are the most widely used measures of predictive power of attributes in scoring models? Motivation: I have a lot of attributes, more than I can study by myself and I want to select somehow the ...
7
votes
6answers
2k views

Algorithms and methods for attribute/feature selection?

I have data with continuous class and I'm searching for good methods to reduce number of attributes. Now I'm using correlation based filters, random forests and Gram–Schmidt algorithm. What I want ...