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8
votes
3answers
210 views

how to represent geography or zip code in machine learning model or recommender system?

I am building a model and I think that geographic location is likely to be very good at predicting my target variable. I have the zip code of each of my users. I am not entirely sure about the best ...
1
vote
0answers
24 views

“Robust” normalization of features from multiple groups and unknown distributions prior to learning

I'm working on a machine learning project involving statistical analysis (and later discriminatory classification) of different proteins (samples) drawn from multiple, potentially overlapping classes ...
0
votes
1answer
13 views

feature representation for DNA bases classification

I'm currently dealing with large DNA sequences for machine learning purposes, I'm basically improving existing methods. What I have is several millions of DNA sequences : ACGTAGGCAGGCTTTC ... In ...
0
votes
0answers
27 views

How to build feature vectors from profile data

I want to build feature vectors from data of my test set, which contains profiles of people. I always want to compare two profiles to each other. Thus my features are: - Same surname ∈ {undefined, ...
0
votes
0answers
18 views

Detecting noisy patterns in document images

I am looking for features to extract to distinguish between text objects and arbitrary noisy patterns in degraded document images. This is an example of a document with some parts of noise, I have ...
0
votes
0answers
10 views

isn't it possible giving weight to attributes in Clementine?

I use SPSS Clementine. I need to give a weight to one of my features to be considered much more than others as it is more important. I can't find any option to do this for training a C5 tree. the ...
1
vote
0answers
37 views

Why do Laplacian Eigenmaps ignore the second KKT condition in the solution calculation?

From the original Laplacian Eigenmaps paper, we find the new manifold embedding $Y$ as follows, given the Laplacian and degree matrices $L$ and $D$: $\arg\min_Ytr(Y^TLY)$ subject to ${Y^TDY=I}$. ...
1
vote
1answer
73 views

How to model features with NULL (not necessarily missing) values

I’d like some advice on my options for modeling features (to be used in R) in the following scenario: I want to make a binary prediction using the positions of the planets as the predictors (the X’s). ...
1
vote
1answer
36 views

Algorithms/methods to create more features of a limited amount of features?

So, let's suppose that I have a set of 20 features - some of them are continous and some of them are binary. Is there an algorithm/method/solution to create more features ( combine/transform ) those ...
1
vote
1answer
35 views

SVM - combining binary and continous representation of the same feature?

How would this influence the accuracy of the SVM model? Let's suppose that I have one variable which max value is 100 and minimum is 0. Currently, I send it to SVM as a single continuous feature, ...
0
votes
1answer
37 views

Add classification filter in Weka

There is a filter in Weka called "Add Classification". One can choose a classification algorithm and then can get the classification labels provided by the model generated by the algorithm, the ...
0
votes
1answer
38 views

How does a linear SVM work? [duplicate]

I have a 2-class problem involving many features. Does a linear support vector machine (SVM) classifier only take into account the values of these features and nothing more? Does it see the ...
1
vote
2answers
89 views

Mixing continuous and binary data with linear SVM?

So I've been playing around with SVMs and I wonder if this is a good thing to do: I have a set of continuous features (0 to 1) and a set of categorical features that I converted to dummy variables. ...
0
votes
0answers
12 views

scikit-learn SkewedChi2Sampler - meaning of skewedness parameter

I am trying to understand the meaning of the "skewedness" parameter for scikit-learn's SkewedChi2Sampler and figure out how this value affects the output of the sampler. I have looked at the docs ...
2
votes
1answer
32 views

Scaling sensor output before training a machine learning model

I'm working with the Emotiv EEG headset. My goal is to collect the signal from its 14 sensors while a subject who is wearing it performs a certain task, and use a machine learning model (a neural ...
0
votes
0answers
63 views

Making feature vector from Gabor filters for classification

My aim is to classify types of cars (Sedans,SUV,Hatchbacks) and earlier I was using corner features for classification but it didn't work out very well so now I am trying Gabor features. code from ...
0
votes
2answers
68 views

Optimal way to creating new features from training set (in R)

My training dataset has 1500 features - all numeric. (But only about 200 data points). I want to create additional features and then use the exhaustive list for feature selection. For creating ...
0
votes
0answers
79 views

Use matrix feature for machine learning or cluster analysis

I have a bunch of features that I would like to use for classification/machine learning and cluster analysis. Normally I use single point values or transformations of values for features and ...
1
vote
2answers
96 views

Understanding Feature Hashing

Wikipedia provides the following example when describing feature hashing; but the mapping does not seem consistent with the dictionary defined For example, to ...
0
votes
0answers
29 views

Confusion related to feature engineering

I was reading this tutorial where they mentioned ...
0
votes
4answers
119 views

Machine learning input relationships

After learning about a few machine-learning models (NN, SVM, decision trees), I was wondering if these models are able to find inherent relationships when learning. For example, if I feed it two ...
0
votes
1answer
51 views

How to convert numerical values to ML feature in the range [0;1]?

I am supposed to extract a bunch of "generally useful" features from a piece of text. Use cases vary, but one could be text categorization. One thing that springs to mind here of course is the length ...
1
vote
1answer
159 views

Conditional restricted Boltzmann machines on a time series dataset

Preamble of the problem I am currently trying to apply Conditional Restricted Boltzmann Machines on a time series dataset problem, in particular, the dataset constitutes of ...
3
votes
0answers
133 views

How to think of features in NLP problems

I am working on a Named Entity Recognition (NER) project. Instead of using an existing library, I decided to implement one from scratch because I wanna learn the basics of how PGMs work under the ...
4
votes
2answers
243 views

How to handle high dimensional feature vector in probability graph model?

I was doing some NLP related stuff which involves training a hidden Markov model, and use the model to segment sentences. For every sentence, I translate the tokens into feature vectors. The features ...
2
votes
1answer
120 views

Adding proportions as features to logistic regression

I'm setting up a logistic regression that models a probability $\mathbb{P}_t$ of certain events. The probability is changing over time and I want to add proportions of past observations that estimate ...
1
vote
0answers
75 views

Mapping data points to a circle

I have some $d$-dimensional data points ($d \ge 2$). I want map them to a circle such that locality is preserved as much as possible. I know that PCA only maps points to a line ($d'=1$) or a plane ...
0
votes
0answers
33 views

Choosing prior distribution in LDA

how do you set prior distribution of K in LDA and can it be used for feature selection to improved selection accuracy of document. Abbey
2
votes
3answers
367 views

Pros/Cons of recoding ordinal/nominal variables to target mean for logistic regression?

Say I have an independent variable with the following relationship to the binary dependent variable, DV: ...
2
votes
1answer
70 views

A buggy but effective feature?

Through error analysis I found that a quite effective feature actually has bugs in its implementation. Correcting the bugs actually decreased the classifier's performance. What do you do? Correct the ...
3
votes
2answers
377 views

Auto crop black borders from a scanned image by making stats about gray values

I'm writing a computer program to automatically detect black noisy borders on scanned images and crop them off. My algorithm is based on 2 variables: gray mean value (of the pixels in a rows/columns) ...
2
votes
3answers
365 views

Machine learning algorithm for ranking

I have got a set of elements $X$ which I can describe according to $n$ characteristics. Thus: $$x_i: \{c_{i1}, c_{i2}, \ldots, c_{in}\} \mid x_i \in X $$ where $c_{ij}$ is the (numerical) evaluation ...
0
votes
2answers
159 views

Calculating distance metrics between a sample set and a point

i have a list of text files and i know that these texts belong to a group, by using this group of text files (i.e this is my sample set) i'd like to calculate Jaccard index and Edit distance for each ...
2
votes
1answer
1k views

What is “feature space”?

What is the definition of "feature space"? For example, When reading about SVMs, I read about "mapping to feature space". When reading about CART, I read about "partitioning to feature space". I ...
1
vote
0answers
28 views

How to stabilize features over time?

I have in my model some features, which seem to have some super power to explain the training data. To be more specific, the model is to predict clicks and the features are the ...
14
votes
1answer
1k views

Tutorials for feature engineering

As is known to all, feature engineering is extremely important to machine learning, however I found few materials associated with this area. I participated to several competitions in Kaggle and ...
5
votes
1answer
825 views

Feature construction and normalization in machine learning

Lets say I want to create a Logistic Classifier for a movie M. My features would be something like age of the person, gender, occupation, location. So training set would be something like: Age ...
2
votes
1answer
250 views

Running logistic regression on a sales data

In R I'm setting up a dataset to run a logistic regression on. I have two question regarding the selection of independent variables. I will first briefly explain the dataset: The dataset contains of ...
0
votes
1answer
148 views

Variance-covariance matrix for structural parameters in simultaneous equation models

The structural form of the linear simultaneous equations model simultaneous equations model can be written as $ \mathbf{y}_{i}^{\prime}\Gamma+\mathbf{x}_{i}^{\prime}\mathbf{B}=\epsilon_{i}^{\prime} ...
0
votes
2answers
59 views

Can I use the output of one neural network as labelled training data?

Is there a term for using the best and worst results of a neural network classifier as labelled input for another neural network? Or isn't this approach valid at all? E.g. I train a neural network ...
1
vote
0answers
73 views

Is it a problem when scale normalization yields lot of zeros?

I use scale normalization for the features of my neural network. As many of the values occur very infrequently, i.e. the minimum count within all samples is often also the count of the feature within ...
1
vote
0answers
159 views

Adaptive Regularization of Weights (AROW): classification dependent on feature index

I am trying to classify texts using a bag of words algorithm. My feature vector is thus a large array of words. In order to build my feature vector, I parse the sample and put a one in the index ...
6
votes
2answers
196 views

Incorporating a treatment into a classification scheme

I have about 400 pieces of silver of different geometric dimensions. They were assigned to six groups and each group went through a series of stress tests, such as bending, pulling, putting in fire ...
3
votes
2answers
500 views

Dealing with mixed categorical data: e.g. Heritage Health Prize data

Looking at the Heritage Health Prize, the data is structured as follows: Each of the Data Sets will be comprised of tables as follows: ...
8
votes
1answer
373 views

Dealing with very large time-series datasets

I have access to a very large dataset. The data is from MEG recordings of people listening to musical excerpts, from one of four genres. The data is as follows: 6 Subjects 3 Experimental repetitions ...
3
votes
2answers
127 views

Feature naming conventions

I'm curious to know what others tend to see as a suitable naming convention for model features or variables, particularly as they relate to their use and reference in software applications. For ...
9
votes
2answers
209 views

Domain-agnostic feature engineering that retains semantic meaning?

Feature engineering is often an important component to machine learning (it was used heavily to win the KDD Cup in 2010). However, I find that most feature engineering techniques either destroy ...
0
votes
1answer
234 views

Discretize frequency of words that follow zipfian distribution

How could I discretize the frequency of words found in a corpus that follow a zipfian distribution? Are there standard methods? It should create bins of exponential-increasing size. My goal is to ...
3
votes
0answers
139 views

Data prep / variable creation for predictive models

I was reading a couple of the write ups from a Kaggle challenge: Here is one and another and it got me wondering about variable creation in data mining and why there seems to exist so few texts or ...
7
votes
3answers
343 views

Feature construction in R

I am wondering if there are any algorithms (perhaps genetic algorithms) in R for feature construction (deriving candidate predictors from existing predictors)? I am thinking of a routine to test ...