The tag has no wiki summary.

learn more… | top users | synonyms

1
vote
0answers
31 views

How can I make sure that an LDA implementation works?

I am currently experimenting with neural nets for classification of on-line handwritten data (hence: not pixels, but time series data). To do so, I use several toolkits (internal development of my ...
1
vote
0answers
13 views

Aggregating features on time from individual samples

I am trying to solve a problem in which I need to figure out what is driving the daily/monthly total expenditure up over time. The data I have is something like this, ...
0
votes
0answers
16 views

Reduction of sparse features for machine learning

I'm trying to use a 1D histogram as a feature for machine learning. A histogram instance can be very sparse and the range of its bins is theoretically unbounded. Moreover it is expected that non-zero ...
0
votes
0answers
26 views

Extracting fixed-length feature vectors from variable-length time series

I have a classification problem where I would like to develop a binary classifier to classify between two different types of objects, given a time-series (signal) related to that object. The problem ...
1
vote
1answer
25 views

How can I calculate cosine distance with multiple feature vectors and weigh them?

I have a dataset of text documents and I'm calculating pairwise cosine distances among them. For each document I have a bag of words vector, a vector built from entities extracted from the document, ...
0
votes
1answer
18 views

How to use a set attributes of an entity at different time snaps to make predictive analysis?

The problem is to come up with a classifier for any task based on a set of attributes of an entity having different values at different times. For instance think about football players and their match ...
0
votes
0answers
16 views

Optional Features

I'm trying to get my head around what I call "optional features" but since I don't know their proper name in statistics I can't find any information about them. Essentially, I'm looking at a problem ...
0
votes
0answers
22 views

Featuring Engineering from Trends in the Training Set

I have a predictive model with just OK performance, and I'm trying to improve it with feature engineering. My question: is it valid to create new features by looking at trends in the training set? ...
0
votes
0answers
11 views

When to cluster features for supervised learning?

I'm doing a project on dog adoption patterns, and I realized that there are many (100 +) different breeds of dogs. I'd like to build a predictive model using breed as covariate, but I'm not sure ...
0
votes
0answers
6 views

Design a feature with time and presence information

Context: I am working on a decision tree classifier, trying to classify businesses as to whether they are likely to have an event occur (default) in the next 90 days. One input I get is whether, and ...
1
vote
0answers
24 views

Walking recognition

I have walking samples from 20 different people. My aim is to detect which walking samples are from which person. I'm trying to achieve this by extracting "walking cycles" from each person's dataset ...
0
votes
0answers
15 views

Mixing Categorical and Continuous variables where cardinality of categorical can surpass data points

Suppose we have a dataset of people that can be described with a mix of some continuous variables (eg height, age) some ordinal (eg social status) and some categorical (eg city, car brand, favourite ...
2
votes
1answer
50 views

What's the optimal way to encode a 'month' feature?

What's the optimal way to encode a 'month' feature? A single integer value or 12 binary values don't quite grasp the concept of modulo distance... Say I want to train an SVM for a certain task and ...
0
votes
1answer
36 views

Quantifying Change in a Histogram Valued Timeseries

I'm attempting to do binary classification where my raw features are collections of histograms that are recorded in a time series. These histograms are scaled to sum to 1. To be more precise and ...
0
votes
0answers
4 views

how to measure the model recovery performance

I have some simulated linear model $y = X\beta+\epsilon$ where $\beta$ is sparse. I am comparing different techniques to recover the structure of $\beta$. I have so 4 values True Positives, False ...
2
votes
3answers
129 views

Best way to turn a date into a numerical feature?

I have a fairly large dataset with a few fields containing time-related data. This data comes in various shapes and sizes, but most of it can be parsed and rephrased in more appropriate formats for ...
0
votes
0answers
29 views

Computing directly comparable wavelet features on variable-length training examples

Consider a classification problem in which the raw data are snippets of a larger 1-D time series signal. In my application, the signal is the response of a motion sensor as a function of time (the raw ...
0
votes
1answer
25 views

Importance of Time Features

if you have a time series and you want to do some predictions, what time feature should you use ? lets say we are trying to predict how many people visit a certain website, we have data for the ...
1
vote
1answer
25 views

Transform a non-monotonic value before decision tree (concrete example)?

Newbie question here. I am building a toy decision tree to differentiate personal names from business, government, or organizational names, like: AAA ENTERPRISES LLC DBA AAA BBB SERVICE SMITH ...
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 ...
0
votes
0answers
7 views

Redundant feature generation

I'm evaluating a few supervised feature selection algorithms, with a focus on redundancy. As a base correlation statistic, I'm using Mutual Information - so discrete variables are also a focus for ...
9
votes
5answers
415 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
80 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
17 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
39 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
27 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
15 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
50 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
137 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
44 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
41 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
81 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
57 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
479 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
21 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
51 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
80 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
95 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
136 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
311 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
4answers
137 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
68 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
379 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
288 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
3answers
389 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
142 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
85 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 ...
2
votes
3answers
457 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
74 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
565 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) ...