I have a data set(Example) of the following type:
Food Type: Chinese, Indian, Thai, Mexican
Ingredient 1: Salt, Chinese Salt
Ingredient 2: Chilli, Red Chilli, Thai Chilli, Green Chilli
Ingredient 3: Turmeric, Cardamom,
Ingredient 4: Chicken, Beef, Fish, Tofu
I have some combinations of data made by hand and I classified them in different food types based on ingredients and recipes. I need to generate more data based on the most probable combinations. One way I have done it so far is to generate all combinations of all ingredients and then classify them into food types based on previous learning. But this approach will not be practical as the data is large. Each category of ingredient can have more than 30-40 values. Moreover, Ingredients are not just 4, they are much more in real data set. I am looking for better ways to generate and classify the data than my already proposed approach. I have applied NB classifier to classify the data. Your help is much appreciated
Since I did not get any replies for over 4 four months, I thought of posting my solution which might help someone else.
The technique I used was to get top five most important features from each of the attribute types(food types in my example). Then I made combinations of all those features. For the rest of the features, I chose a value randomly. This generated new data which was manageable in size.
If you need any clarifications, please feel free to ask.
Related
I am trying to build a content based recommendation model but I am stuck on how to proceed with which algorithm to choose from. Basically, my features are user_id, user_age, gender, location, show_id, show_type(eg: series, movies etc.), show_duration, user_watched_duration, genre, rating. And my model has to predict top 5 recommendation shows for certain users.
The proportion of users to shows is hugh. Like there are only around 10k users but the shows that are mapped to each user is approx 150. So each user has an history of 150 shows mapped to them on an average. So total records are 10k x 150 = 15,00,000
Now, I am confused like which algorithm to proceed with this scenario. I read content based method is the ideal for my scenario. But when I checked SVD from surprise library, it is only taking 3 features as input DataSet - "user_id", "item_id", "rating" and fitting the model. But I have to consider fitting other features like user_watched_duration out of the show_duration and give preference if he/she has fully watched the show. And similarly, I want the model to consider recommending based on the Gender and age also. Like for example, for young men, (male < 20 years old) have watched a show and given a higher rating, then my recommendation to user's of similar category of young men should be given.
Can I use to train a normal classifical model like KNN for this? I tried to think of using sparse matrix using csr_matrix with row consisting of user_id and col consisting of show_id. And then transposing using (user_show_matrix.T * user_show_matrix) , so that I can use this to get counts of shows watched for that particular user. But the problem with this approach is that I cannot map other features with this, right?
So please suggest how to proceed. I already did data cleaning, label encoded categories etc. Will I be able to use any classification algorithms for this? Appreciate any references on similar approaches. Thank you!
I am trying to solve a problem where I'm identifying entities in articles (ex: names of cars), and trying to predict sentiment about each car within the article. For that, I need to extract the text relevant to each entity from within the article.
Currently, the approach I am using is as follows:
If a sentence contains only 1 entity, tag the sentence as text for that entity
If sentence has more than 1 entity, ignore it
If sentence contains no entity, tag as a sentence for previously identified entity
However, this approach is not yielding accurate results, even if we assume that our sentiment classification is working.
Is there any method that the community may have come across that can solve this problem?
The approach fails for many cases and gives wrong results. For example if I am saying - 'Lets talk about the Honda Civic. The car was great, but failed in comparison to the Ford focus. The car also has good economy.'
Here, the program would pick up Ford Focus as the entity in last 2 sentences and tag those sentences for it.
I am using nltk for descriptive words tagging, and scikit-learn for classification (linear svm model).
If anyone could point me in the right direction, it would be greatly appreciated. Is there some classifier I could build with custom features that can detect this type of text if I were to manually tag say - 50 articles and the text in them?
Thanks in advance!
Recently I'm working on my course project, it's an android app that can automatically help fill consuming form based on the user's voice. So here is one sample sentence:
So what I want to do is let the app fill forms automatically, my forms have several fields: time(yesterday), location(MacDonald), cost(10 dollars), type(food). Here the "type" field will include food, shopping, transport, etc.
I have used the word-splitting library to split the sentence into several parts and parse it, so I can already extract the time, location and cost fields from the user's voice.
What I want to do is deduce the "type" field with some kind of machine learning model. So there should be some records in advance, input by user manually to train the model. After training, when new record comes in, I first extract the time, location and cost fields, and then calculate the type field based on the model.
But I don't know how to represent the location field, should I use a dictionary to include many famous locations and use index to represent the location? If so, which kind of machine learning method should I use to model this requirement?
I would start with the Naive Bayes classifier. The links below should be useful in understanding it:
http://en.wikipedia.org/wiki/Naive_Bayes_classifier
http://cs229.stanford.edu/notes/cs229-notes2.pdf
http://scikit-learn.org/stable/modules/naive_bayes.html
I wonder if time and cost are that discriminative/informative in comparison to location for your task.
In general, look at the following link on working with text data (it should be useful even if you dont know python):
http://scikit-learn.org/dev/tutorial/text_analytics/working_with_text_data.html
It should include three stages:
Feature Representation:
One way to represent the features is the Bag-of-Word representation, which you fix an order of the dictionary and use a word frequency vector to represent the documents. See https://en.wikipedia.org/wiki/Bag-of-words_model for details.
Data and Label Collection:
Basically, in this stage, you should prepare some [feature]-[type] pairs to training your model, which can be tedious or expensive. If you had already published your app, and collected a lot of [sentence]-[type] pair (probably chosen by app user), you can extract the features and build a training set.
Model Learning:
Cdeepakroy has suggested a good choice of the model: Naive Bayes, which is very efficient for classification task like this. At this stage, you can just find a suitable package, insert your training data, and enjoy the classifier it returns.
I am a newbee in the field of data mining. I am working on very interesting Data Minign problem. Data description is as follows:
Data is time sensitive. Item attributes are dependent on time factor as well as its class label. I am grouping weekly data as one instance of training or test record. Each week, some of the item attributes may change along with its Popularity(i.e. Class label).
Some sample data as below:
IsBestPicture,MovieID,YearOfRelease,WeekYear,IsBestDirector,IsBestActor,IsBestActress,NumberOfNominations,NumberOfAwards,..,Label
-------------------------------------------------
0_1,60000161,2000,1,9-00,0,0,0,0,0,0,0
0_1,60004480,2001,22,19-02,1,0,0,11,3,0,0
0_1,60000161,2000,5,13-00,0,0,0,0,0,0,1
0_1,60000161,2000,6,14-00,0,0,0,0,0,0,0
0_1,60000161,2000,11,19-00,0,0,0,0,0,0,1
My research advisor suggested to use Naive Bayes algorithm which can adapt such dynamic data that is changing with time.
I am using data from 2000-2004 as Training an 2005 as Testing. If i include Week-Year attribute in my items data set, then it will cause 0 probability in Naive Bayes. Is it ok to omit this attribute from my data set after organizing my data in chronological order?
Moreover, how to adapt my model as i read new test cases ? as the new test cases might cause change in Class label ?
Can you provide a little more insight into your methods? For instance, are you using R, SPSS, Python, SQL Server 2008R2, or RapidMiner 5.2? And if you can include a very small (3-4 row segment) of some of your data, that would help people figure out how to tackle this.
One immediate approach to get an idea of what you are looking at would be to do a Random Forest/Decision Tree and K-Means clustering in order to determine common seperation points in the data. Have you begun by a quick glance at the data's histograms, averages, and outliers?
It's known how collaborative filtering (CF) is used for movie, music, book recommendations. In the paper 'Collaborative Topic Modeling for Recommending Scientific Articles' among other things authors show an example of collaborative filtering applied to ~5,500 users and ~17,000 scientific articles. With ~200,000 user-item pairs, the user-article matrix is obviously highly sparse.
What if you do collaborative filtering with matrix factorization for, say, all news articles shared on Twitter? The matrix will be even sparser (than that in the scientific articles case) which makes CF not very applicable. Of course, we can do some content-aware analysis (taking into account, the text of an article), but that's not my focus. Or we can potentially limit our time window (focus, say, on all news articles shared in the last day or week) to make the user-article matrix denser. Any other ideas how to fight the fact that the matrix is very sparse? What are the results in research in the area of CF for news article recommendations? Thanks a lot in advance!
You might try using an object-to-object collaborative filter instead of a user-to-object filter. Age out related pairs (and low-incidence pairs) over time since they're largely irrelevant in your use case anyway.
I did some work on the Netflix Prize back in the day, and quickly found that I could significantly outperform the base model with regard to predicting which items were users' favorites. Unfortunately, since it's basically a rank model rather than a scalar predictor, I didn't have RMSE values to compare.
I know this method works because I wrote a production version of this same system. My early tests showed that, given a task wherein 50% of users' top-rated movies were deleted, the object-to-object model correctly predicted (i.e., "replaced") about 16x more of users' actual favorites than a basic slope-one model. Plus the table size is manageable. From there it's easy to include a profitability weight against the sort order, etc. depending on your application.
Hope this helps! I have a working version in production but am still looking for beta clients to bang on the system... if anyone has time to give it a run I'd love to hear from you.
Jeb Stone, PhD
www.selloscope.com