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1answer
25 views

How to do machine learning (regression/classification) when the samples are of different sizes?

In standard cookbook machine learning, we operate on a rectangular matrix; that is, all of our data points have the same number of features. How do we cope with situations in which all of our data ...
0
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1answer
30 views

Automatic feature building/extraction

I have a large time stamped data set (several millions of rows), with known measured inputs xi, where i is a large number to the order of magnitude of 20. The goal is to predict a response yi given ...
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0answers
8 views

Need to order a set of colors to build a feature vector

I am working on Computer Vision task of object classification with python and OpenCV. Currently I am extracting some characteristic colors of an image using K-means clustering on all the pixel to ...
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0answers
7 views

Is there an algorithm to determine if too few features are selected for k-nearest-neighbor?

Is there an algorithm to determine if too few features are selected for k-nearest-neighbor when no test set is available, --when the input vectors are unknowns? Here's my problem, I have a massive ...
2
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2answers
137 views

Machine learning feature encoding

I'm new to Machine Learning. I've just finished the Coursera course. :) And for my first practical attempt I wanted to "analyse" a local used cars selling website in order to compose a modal that ...
0
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1answer
25 views

Using a set as a feature in decision tree classification

I'm faced with a data set where one of the features is a set of 4-5 categories (this number of categories isn't constant). I need to use this feature for building a decision tree. I searched online ...
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0answers
20 views

Statistical technique for data when three interventions are administrated and multiple responses measuring different constructs are measured

I have a data where three interventions were administrated on subjects and different response variables (say 15 variables) on Likert Scale were observed. These response variables measure three ...
4
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2answers
202 views

Autoencoders can't learn meaningful features

I have 50,000 images such as these two: They depict graphs of data. I wanted to extract features from these images so I used autoencoder code provided by Theano (deeplearning.net). The problem ...
0
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1answer
33 views

pairwise distances used as features for classification

I have a feature matrix 977x3 features = rand(977,3); where each row is an observation and each column is a feature. I calculate the pairwise distances between ...
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0answers
23 views

DecisionTreeClassifier scikit-learn : knowing the leaf to which an example belongs to

I am currently reading this paper http://quinonero.net/Publications/predicting-clicks-facebook.pdf , where they are using trees to generate feature that are afterwards fed to a linear classifier. My ...
3
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1answer
60 views

Optimal construction of day feature in neural networks

Working on regression problem I started to think about representation of "day of a week" feature. I wonder which approach would perform better: one feature; value 1/7 for Monday; 2/7 for Tuesday... ...
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0answers
27 views

What's the potential reason that by combining two feature sets the performance of random forest dropped?

I am building random forests on high dimensional, sparse, and class unbalanced training datasets (around 500 - 5000 examples) using two different feature sets. I did stratified 10-fold cross ...
1
vote
1answer
49 views

Convolutional neural network - Using absolute of tanh on convolution output

I've watched an online lecture regarding CNN (https://www.youtube.com/watch?v=wORlSgx0hZY) that confused me a bit. At roughly 8:35 in the lecture it was stated that it is important to use the absolute ...
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1answer
44 views

NLP tokenization for building feature vector

I am trying to match new product description with the existing ones. Product description looks like this: Panasonic DMC-FX07EB digital camera silver. These are steps to be performed: Tokenize ...
2
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0answers
40 views

How features are representation in deep learning

I'm trying to build a deep neural network for mobile phone data. I've been doing different tutorials, but there are some basics concerning how the hidden units represents features that I really would ...
0
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0answers
27 views

Hashing functions in NLP

I have been reading a lot of papers about nlp which use the hashing trick, and I came across a lot of sentences like : "We take k hashing functions to hash words or bi-grams". And after that they ...
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0answers
37 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 ...
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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, ...
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0answers
24 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 ...
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0answers
90 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
40 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
22 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
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0answers
20 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 ...
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0answers
26 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? ...
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0answers
13 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 ...
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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 ...
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0answers
26 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 ...
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0answers
31 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
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1answer
57 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
47 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 ...
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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
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3answers
176 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
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0answers
42 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
29 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
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1answer
31 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 ...
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0answers
57 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 ...
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0answers
8 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 ...
10
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6answers
538 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 ...
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0answers
109 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
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1answer
21 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 ...
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0answers
46 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
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0answers
30 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
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0answers
17 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
1answer
59 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
145 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
51 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
50 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
164 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
73 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 ...
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2answers
1k 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. ...