I have time series data of size 100000*5. 100000 samples and five variables.I have labeled each 100000 samples as either 0 or 1. i.e. binary classification.
I want to train it using LSTM , because of the time series nature of data.I have seen examples of LSTM for time series prediction, Is it suitable to use it in my case.
Not sure about your needs.
LSTM is best suited for sequence models, like time series you said, and your description don't look a time series.
Any way, you may use LSTM for time series, not for prediction, but for classification like this article.
In my experience, for binary classification having only 5 features you could find better methods, will consume more memory thant other methods, and could get worst results.
First of all, you can see it from a different perspective, i.e. instead of having 10,000 labeled samples of 5 variables, you should treat it as 10,000 unlabeled samples of 6 variables, where the 6th variable is the label.
Therefore, you can train your LSTM as a multivariate predictor for your 6th variable, that is the sample label and compare with the ground truth during testing to evaluate its performance.
Related
I have a dataset with thousand of sentences belonging to a subject. I would like to know what would be best to create a classifier that will predict a text as "True" or "False" depending on whether they talk about that subject or not.
I've been using solutions with Weka (basic classifiers) and Tensorflow (neural network approaches).
I use string to word vector to preprocess the data.
Since there are no negative samples, I deal with a single class. I've tried one-class classifier (libSVM in Weka) but the number of false positives is so high I cannot use it.
I also tried adding negative samples but when the text to predict does not fall in the negative space, the classifiers I've tried (NB, CNN,...) tend to predict it as a false positive. I guess it's because of the sheer amount of positive samples
I'm open to discard ML as the tool to predict the new incoming data if necessary
Thanks for any help
I have eventually added data for the negative class and build a Multilineal Naive Bayes classifier which is doing the job as expected.
(the size of the data added is around one million samples :) )
My answer is based on the assumption that that adding of at least 100 negative samples for author’s dataset with 1000 positive samples is acceptable for the author of the question, since I have no answer for my question about it to the author yet
Since this case with detecting of specific topic is looks like particular case of topics classification I would recommend using classification approach with the two simple classes 1 class – your topic and another – all other topics for beginning
I succeeded with the same approach for face recognition task – at the beginning I built model with one output neuron with high level of output for face detection and low if no face detected
Nevertheless such approach gave me too low accuracy – less than 80%
But when I tried using 2 output neurons – 1 class for face presence on image and another if no face detected on the image, then it gave me more than 90% accuracy for MLP, even without using of CNN
The key point here is using of SoftMax function for the output layer. It gives significant increase of accuracy. From my experience, it increased accuracy of the MNIST dataset even for MLP from 92% up to 97% for the same model
About dataset. Majority of classification algorithms with a trainer, at least from my experience are more efficient with equal quantity of samples for each class in a training data set. In fact, if I have for 1 class less than 10% of average quantity for other classes it makes model almost useless for the detection of this class. So if you have 1000 samples for your topic, then I suggest creating 1000 samples with as many different topics as possible
Alternatively, if you don’t want to create a such big set of negative samples for your dataset, you can create a smaller set of negative samples for your dataset and use batch training with a size of batch = 2x your negative sample quantity. In order to do so, split your positive samples in n chunks with the size of each chunk ~ negative samples quantity and when train your NN by N batches for each iteration of training process with chunk[i] of positive samples and all your negative samples for each batch. Just be aware, that lower accuracy will be the price for this trade-off
Also, you could consider creation of more generic detector of topics – figure out all possible topics which can present in texts which your model should analyze, for example – 10 topics and create a training dataset with 1000 samples per each topic. It also can give higher accuracy
One more point about the dataset. The best practice is to train your model only with part of a dataset, for example – 80% and use the rest 20% for cross-validation. This cross-validation of unknown previously data for model will give you a good estimation of your model accuracy in real life, not for the training data set and allows to avoid overfitting issues
About building of model. I like doing it by "from simple to complex" approach. So I would suggest starting from simple MLP with SoftMax output and dataset with 1000 positive and 1000 negative samples. After reaching 80%-90% accuracy you can consider using CNN for your model, and also I would suggest increasing training dataset quantity, because deep learning algorithms are more efficient with bigger dataset
For text data you can use Spy EM.
The basic idea is to combine your positive set with a whole bunch of random samples, some of which you hold out. You initially treat all the random documents as the negative class, and train a classifier with your positive samples and these negative samples.
Now some of those random samples will actually be positive, and you can conservatively relabel any documents that are scored higher than the lowest scoring held out true positive samples.
Then you iterate this process until it stablizes.
I am using Jason Brownlee's tutorial (mirror) to apply LSTM network on some syslog/network log data. He's a master!
I have syslog data(a specific event) for each day for last 1 year and so I am using LSTM network for time series analysis. I am using LSTM from Keras deep learning library.
As I understand -
About Batch_size
A batch of data is a fixed-sized number of rows from the training
dataset that defines how many patterns to process before updating
the weights of the network. Based on the batch_size the Model
takes random samples from the data for the analysis. For time series
this is not desirable, hence the batch_size should always be 1.
About setting value for shuffle value
By default, the samples within an epoch are shuffled prior to being exposed to the network. This is undesirable for the LSTM
because we want the network to build up state as it learns across
the sequence of observations. We can disable the shuffling of
samples by setting “shuffle” to “False“.
Scenario1 -
Using above two rules/guidelines - I ran several trials with different number of neurons, epoch size and different layers and got better results from the baseline model(persistence model).
Scenario2-
Without using above guidelines/rules - I ran several trials with different number of neurons, epoch size and different layers and got even better results than Scenario 1.
Query - Setting shuffle to True and Batch_size values to 1 for time series. Is this a rule or a guideline?
It seems logical reading the tutorial that the data for time series should not be shuffled as we do not want to change the sequence of data, but for my data the results are better if I let the data be shuffled.
At the end what I think, what matters is how I get better predictions with my runs.
I think I should try and put away "theory" over concrete evidence, such as metrics, elbows, RMSEs,etc.
Kindly enlighten.
It depends a lot on the size of your data, also in the number of variables, decreasing batch size in my experience gives better results since the update is more frequent but in huge datasets it is very expensive. And you have to play with this trade-off (training time vs result).
About your shuffle it may be the case that your data is not that correlated with the past, if that is the case shuffling the data helps the network to learn and be able to generalize (like ordered by label) check reason 7 of the following 37 reasons your neural network not working
Batch size the larger the difficult it is to generalize (reason 11). When data clearly depends on the past you can declare your LSTM in Keras to stateful, this means: "that the states computed for the samples in one batch will be reused as initial states for the samples in the next batch" according to Keras API. Hope this helps.
I have train dataset and test dataset from two different sources. I mean they are from two different experiments but the results of both of them are same biological images. I want to do binary classification using deep CNN and I have following results on test accuracy and train accuracy. The blue line shows train accuracy and the red line shows test accuracy after almost 250 epochs. Why the test accuracy is almost constant and not raising? Is that because Test and Train dataset are come from different distributions?
Edited:
After I have add dropout layer, reguralization terms and mean subtraction I still get following strange results which says the model is overfitting from the beginning!
There could be 2 reasons. First you overfit on the training data. This can be validated by using the validation score as a comparison metric to the test data. If so you can use standard techniques to combat overfitting, like weight decay and dropout.
The second one is that your data is too different to be learned like this. This is harder to solve. You should first look at the value spread of both images. Are they both normalized. Matplotlib normalizes automatically for plotted images. If this still does not work you might want to look into augmentation to make your training data more similar to the test data. Here I can not tell you what to use, without seeing both the trainset and the testset.
Edit:
For normalization the test set and the training set should have a similar value spread. If you do dataset normalization you calculate mean and std on training set. But you also need to use those calculated values on the test set and not calculate the test set values from the test set. This only makes sense if the value spread is similar for both the training and test set. If this is not the case you might want to do per sample normalization first.
Other augmentation that are commonly used for every dataset are oversampling, random channel shifts, random rotations, random translation and random zoom. This makes you invariante to those operations.
I am working on a classification problem, which has different sensors. Each sensor collect a sets of numeric values.
I think its a classification problem and want to use weka as a ML tool for this problem. But I am not sure how to use weka to deal with the input values? And which classifier will best fit for this problem( one instance of a feature is a sets of numeric value)?
For example, I have three sensors A ,B, C. Can I define 5 collected data from all sensors,as one instance? Such as, One instance of A is {1,2,3,4,5,6,7}, and one instance of B is{3,434,534,213,55,4,7). C{424,24,24,13,24,5,6}.
Thanks a lot for your time on reviewing my question.
Commonly the first classifier to try is Naive Bayes (you can find it under "Bayes" directory in Weka) because it's fast, parameter less and the classification accuracy is hard to beat whenever the training sample is small.
Random Forest (you can find it under "Tree" directory in Weka) is another pleasant classifier since it process almost any data. Just run it and see whether it gives better results. It can be just necessary to increase the number of trees from the default 10 to some higher value. Since you have 7 attributes 100 trees should be enough.
Then I would try k-NN (you can find it under "Lazy" directory in Weka and it's called "IBk") because it commonly ranks amount the best single classifiers for a wide range of datasets. The only issues with k-nn are that it scales badly for large datasets (> 1GB) and it needs to fine tune k, the number of neighbors. This value is by default set to 1 but with increasing number of training samples it's commonly better to set it up to some higher integer value in range from 2 to 60.
And finally for some datasets where both, Naive Bayes and k-nn performs poorly, it's best to use SVM (under "Functions", it's called "Lib SVM"). However, it can be hassle to set up all the parameters of the SVM to get competitive results. Hence I leave it to the end when I already know what classification accuracies to expect. This classifier may not be the most convenient if you have more than two classes to classify.
I am doing remote sensing image classification. I am using the object-oriented method: first I segmented the image to different regions, then I extract the features from regions such as color, shape and texture. The number of all features in a region may be 30 and commonly there are 2000 regions in all, and I will choose 5 classes with 15 samples for every class.
In summary:
Sample data 1530
Test data 197530
How do I choose the proper classifier? If there are 3 classifiers (ANN, SVM, and KNN), which should I choose for better classification?
KNN is the most basic machine learning algorithm to paramtise and implement, but as alluded to by #etov, would likely be outperformed by SVM due to the small training data sizes. ANNs have been observed to be limited by insufficient training data also. However, KNN makes the least number of assumptions regarding your data, other than that accurate training data should form relatively discrete clusters. ANN and SVM are notoriously difficult to paramtise, especially if you wish to repeat the process using multiple datasets and rely upon certain assumptions, such as that your data is linearly separable (SVM).
I would also recommend the Random Forests algorithm as this is easy to implement and is relatively insensitive to training data size, but I would advise against using very small training data sizes.
The scikit-learn module contains these algorithms and is able to cope with large training data sizes, so you could increase the number of training data samples. the best way to know for sure would be to investigate them yourself, as suggested by #etov
If your "sample data" is the train set, it seems very small. I'd first suggest using more than 15 examples per class.
As said in the comments, it's best to match the algorithm to the problem, so you can simply test to see which algorithm works better. But to start with, I'd suggest SVM: it works better than KNN with small train sets, and generally easier to train then ANN, as there are less choices to make.
Have a look at below mind map
KNN: KNN performs well when sample size < 100K records, for non textual data. If accuracy is not high, immediately move to SVC ( Support Vector Classifier of SVM)
SVM: When sample size > 100K records, go for SVM with SGDClassifier.
ANN: ANN has evolved overtime and they are powerful. You can use both ANN and SVM in combination to classify images
More details are available #semanticscholar.org