1
vote
0answers
8 views

Optimal classification model for translating words

I have the following problem: I have a set of English words which I want to translate to Dutch. Of each words I mined a set of possible translations. For example, for the word "Eighteen" I obtained ...
1
vote
1answer
53 views

Linear regression of 0/1 response (Fig. 2.1 of The elements of statistical learning)

In chapter 2 ESL book authors write: Let's look at example of linear model in a classification context They fit a simple linear model $g = 0.3290614 -0.0226360\cdot x_1 + 0.2495983 \cdot x_2 + e$, ...
0
votes
2answers
28 views

Instance weighing in libsvm/liblinear

I often use the instance weights with Libsvm for classification problems. http://www.csie.ntu.edu.tw/~cjlin/libsvmtools/#weights_for_data_instances Does anyone know the details of the algorithm that ...
0
votes
2answers
30 views

The role of the bias terms in matrix factorization formulas?

I'm reading about matrix factorization for recommender systems. A basic matrix factorization model would be something like: $(p_i \times q_j ) + b_i + b_j$. That formula would compute the rating for ...
0
votes
0answers
31 views

different feature types for classification

There has a data set with several features. One feature is of the type of continuous numerical values; another feature is of the type of categorical values, such as A, B and C. If I want to build a ...
0
votes
0answers
29 views

Which statistics to use in order to understand a dataset?

So I have a dataset that I will use to train a bunch of classifiers. I need to do that for my thesis. However I'm not sure which statistics are good to use to better understand the dataset and the ...
2
votes
2answers
77 views

Weighting words based on position in text

I'm currently working on semantic analysis and had a question about text organization and structure. Are there any algorithms, or statistical / machine-learning models that weight the importance of a ...
0
votes
1answer
60 views

Calculating the information gain on the features with python

I'm looking for a python library that computes the information gain for the features given a training matrix. Are you aware of any?
0
votes
0answers
24 views

semisupervised classification training on all or part of unlabeled data

I have 3 sets of data. A positively labeled dataset. An unlabeled dataset that has for sure positive (around 75%) and negative data. An unlabeled dataset that has for sure positive data and maybe ...
0
votes
0answers
8 views

S and N parameters of Ridor

I am using WEKA and in particular their implementation of Ridor. The documentation says this about the parameters S and N: -S Set number of shuffles to randomize the data in order to get better ...
1
vote
0answers
24 views

Low pass filter to maintain edge information

I am looking for a kernel as low pass filter that satisfy as:I must find a kernel that statisfies as follows: In the my reference paper, the author suggest gaussian kernel that is The gaussian ...
0
votes
0answers
9 views

Represents istances with multiple values for an attribute and similarity between them

In the scenario in which I'm working each entity could be represented in terms of ten distinct properties that I will call p1, p2, ..., pn. For each of them, an entity, can have its specific range of ...
0
votes
0answers
31 views

Anomaly detection of web browsing sequences

Please consider that I'm quite new to machine learning. I need to create models based on browsing patterns of web users and find deviations from that model. I'm using web server access log files. For ...
0
votes
0answers
20 views

Propensity in linear models and bilinear regression models

I'm reading this paper about matrix factorization. In the paper they want to combine the features of the nodes in the model (page 6). First they illustrate the simple idea of combining the features of ...
1
vote
1answer
32 views

Intuition behind matrix factorization formulations?

I'm reading this paper about matrix factorization. In the paper they propose to use this factorization for the adjacency (or similarity) matrix $G$ using the following formulation: $G = U \Lambda U^T$ ...
14
votes
7answers
2k views

What is the daily job routine of the machine learning scientist?

I'm a master CS student in a German university now writing my thesis. I will be done in two months I have to make the very hard decision if I should continue with a PhD or find a job in the industry. ...
1
vote
2answers
154 views

High precision with low recall SVM

I'm classifying a data set using SVM and those are the precision and recall values for two classes. ...
1
vote
0answers
33 views

Difference between Factorization machines and Matrix Factorization?

I came across the term Factorization Machines in recommender systems. I know what Matrix Factorization is for recommender systems but never heard of Factorization Machines. So what's the difference?
0
votes
1answer
17 views

Assumption behind few latent features in recommender systems?

I know in recommender systems you have a rating matrix and then you factorize this matrix into two matrices and then learn those matrices with gradient descent. In those matrices we specify the number ...
0
votes
1answer
43 views

Approach for mapping consumer preferences

I have this web application where I need to map consumer preferences based on some input information and individual choices. My goal is to create a list of product recommendations and evaluate the ...
1
vote
1answer
38 views

Meaning of latent features?

I'm trying to understand matrix factorization models for recommender systems and I always read 'latent features', but what does that mean? I know what a feature means for a training dataset but I'm ...
0
votes
0answers
16 views

A framework for comparing the performance of Expectation Maximization

I have my own implementation of the Expectation Maximization (EM) algorithm based on the following paper http://pdf.aminer.org/000/221/588/fuzzy_k_means_clustering_with_crisp_regions.pdf I would like ...
0
votes
1answer
106 views

What data mining/machine learning approach to use for a scoring model?

Suppose I have a large data set with lots of features(attributes). And I'm tasked to build some kind of scoring model to rank certain objects with all these features. How do I go about doing this? ...
3
votes
1answer
55 views

When is it appropriate to use PCA as a preprocessing step?

I understand that PCA is used for dimensionality reduction to be able to plot datasets in 2D or 3D. But I have also seen people applying PCA as a preprocessing step in classification scenarios where ...
0
votes
0answers
27 views

Calculating the variance of a model?

I often hear about the bias-variance tradeoff to evaluate classifiers. Now I want to calculate them. I often compute the AUC of a binary classifier to evaluate its performance and do a 10-fold ...
4
votes
2answers
165 views

Ensembles of Ensembles?

I like the idea of ensemble learners, especially Bagging, but I always wondered as why they are not the most powerful learners since they have a clean motivation. I don't have the answer to that ...
3
votes
1answer
107 views

The usage of data mining in pharmaceutical companies?

I know that data mining applications are being used in pharmaceutical companies, but my question is: what do they use them for? Sometimes I read: "drug discovery", but how? How is it used for drug ...
0
votes
1answer
46 views

Data preparation and machine algo for ad click prediction

I am an ml noob. I have a task at hand of predicting click probability given user information like city, state, os version, os family, device, browser family browser version, city, etc. I have been ...
0
votes
1answer
21 views

How to increase a particular terms's weightage?

I am doing Text classification using LibSVM in Rapid Miner. I am using TFIDF values for processing documents. I need to Increase weightage of some terms in the documents(for eg. words in BOLD and ...
3
votes
1answer
73 views

Inputs to k-means are often normalized per-feature. Why not fully whiten the data instead?

We often normalize inputs to the k-means algorithm by 1) subtracting the mean on a per-feature basis and 2) dividing by the standard deviation on a per-feature basis. Some rational behind this is ...
46
votes
8answers
6k views

Skills hard to find in machine learners?

It seems that data mining and machine learning became so popular that now almost every CS student knows about classifiers, clustering, statistical NLP ... etc. So it seems that finding data miners is ...
2
votes
2answers
82 views

Matrix Factorization algorithms for Recommender Systems

I need to learn about Matrix Factorization for recommender systems, so I downloaded this paper https://datajobs.com/data-science-repo/Recommender-Systems-[Netflix].pdf but I found it too shallow. It ...
0
votes
0answers
31 views

What are the mathematics I need to learn, before I start research in data mining [duplicate]

I usually use text mining, graph mining, Information retrieval, and natural lanuage processing. Also i will use the fundamental concepts of data mining like classification, association and clustering. ...
1
vote
0answers
73 views

What should I use - Multi label classification or Multi class classification?

In my dataset, I have 2 labels, positive and negative. Most samples belong to only one class, either positive or negative. A small fraction of samples take both labels i.e. both positive and negative. ...
0
votes
0answers
24 views

Association Rules “with a kind of class”

I want to use/adapt a recommendation algorithm for posters in an e-commerce. The thing is that I want to use previous categories searched before posting in a particular category (has to be at a very ...
0
votes
0answers
76 views

SVD application for a Boolean sparse Matrix

Basically, I am trying to have a recommender system based on SVD for a Boolean utility matrix. ie If at all some entries are present in the utility matrix, they will be 1 (I made it pseudo-implicit ...
0
votes
1answer
74 views

Question on Machine Learning Overview?

I am a researcher from India and working in the field of Computational Linguistics for quite some time. I have lately started working in the field of Machine Learning based algorithms. To do this I ...
0
votes
0answers
54 views

Examples for commercial applications of data mining in pharmacy

I've always heard that data mining and machine learning tools/techniques are heavily used in the pharmacy sector and biology but I have never heard of companies that offer commercial applications of ...
0
votes
0answers
28 views

In which Data Stream Mining Algorithms do Damped Windows make sense?

For Data Stream Mining, especially in Document Classification, the most common ML algorithms are Multinomial Naive Bayes, Stochastic Gradient Descent and Ozbag (ADWIN). When looking at their ageing ...
0
votes
1answer
68 views

encrypted data on CART, ID3

Some data are confidential such as patient data. Therefore sometimes companies does not want to give original patient data instead they first encrypt it(for instance with SHA1) and then give. If we ...
0
votes
0answers
39 views

Introduction to recurrent neural networks?

I have two questions: 1- What are the applications of recurrent neural networks? 2- Can you recommend some good resources/papers/tutorials that introduce recurrent neural networks?
0
votes
0answers
44 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 ...
7
votes
2answers
319 views

Assessing principal components analysis

What is a good metric for assessing the quality of a pca? I performed this algorithm on a dataset. My objective was to reduce the number of features (the information was very redundant). I know the ...
0
votes
0answers
51 views

R choosing the right classification approach for $ transaction volume categories

Our customers are Merchants and use our online payment service. Before they started using our service, they indicated how much $ transaction volume they will make per year. However this turned out to ...
0
votes
0answers
27 views

Calculate similarity of waiting times of users

Let's say I have waiting times(seconds) of users in web pages. ...
1
vote
1answer
59 views

Is f-measure synonymous with accuracy?

I understand that f-measure (based on precision and recall) is an estimate of how accurate a classifier is. Also, f-measure is favored over accuracy when we have an unbalanced dataset. I have a simple ...
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
1answer
57 views

How to think while comparing methods

I'm a CS master student focusing on data mining. Now I'm doing my master thesis and the contribution of my thesis is to compare different approaches/methods of one topic (e.g. clustering of text ...
1
vote
0answers
37 views

Wilcoxon-Mann-Whitney as a loss function

I'm reading a paper where the authors are using Wilcoxon-Mann-Whitney loss function while minimizing an objective function. As the authors say in the paper, the role of the loss function is to give a ...
0
votes
0answers
43 views

Should I use multi-label classification?

I have a classification problem with 2 classes (positive and negative). Usually, in such classification problems, all the samples will be labelled either 'positive' or 'negative'. In my dataset, some ...