I am using Weka and applying J48 to build my classifier. I have 40 features with 2000 instances (700 class a and 1300 class b).
The J48 decision tree is just using 2 features out of 40! Is there anyway to allow J48 to use all features or is there any other algorithm that allows using all features?
Thanks in advance.
Maybe it is because J48 does not need more attributes.
You can check feature's correlation in Select attribute tab, and run the selector with Ranker as search method and Principal Components as evaluator. It will show you the relations between each feature and each class, and it will also tell you which are the features that best describe your classes.
It is not necessary that all the 40 features are needed for the classification. Because some features might be redundant (e.g. correlated) or does not contain discriminatory information.
You can run feature selection before from the Select attributes tab in Weka Explorer and see which features are important.
Also you can test classifiers such as SVM (libSVM or SMO), Neural Network ( MultilayerPerceptron) and/or Random Forest as they tend to give the best classification results in general (problem dependent)
Related
I have a imbalanced dataset. I am using SMOTE (Synthetic Minority Oversampling Technique)to perform oversampling. When performing the binary classification, I use 10-fold cross validation on this oversampled dataset.
However, I recently came accross this paper; Joint use of over- and under-sampling techniques and cross-validation for the development and assessment of prediction models that mentions that it is incorrect to use the oversampled dataset during cross-validation as it leads to overoptimistic performance estimates.
I want to verify the correct approach/procedure of using the over-sampled data in cross validation?
To avoid overoptimistic performance estimates from cross-validation in Weka when using a supervised filter, use FilteredClassifier (in the meta category) and configure it with the filter (e.g. SMOTE) and classifier (e.g. Naive Bayes) that you want to use.
For each cross-validation fold Weka will use only that fold's training data to parameterise the filter.
When you do this with SMOTE you won't see a difference in the number of instances in the Weka results window, but what's happening is that Weka is building the model on the SMOTE-applied dataset, but showing the output of evaluating it on the unfiltered training set - which makes sense in terms of understanding the real performance. Try changing the SMOTE filter settings (e.g. the -P setting, which controls how many additional minority-class instances are generated as a percentage of the number in the dataset) and you should see the performance changing, showing you that the filter is actually doing something.
The use of FilteredClassifier is illustrated in this video and these slides from the More Data Mining with Weka online course. In this example the filtering operation is supervised discretisation, not SMOTE, but the same principle applies to any supervised filter.
If you have further questions about the SMOTE technique I suggest asking them on Cross Validated and/or the Weka mailing list.
The correct approach would be first splitting the data into multiple folds and then applying sampling just to the training data and let the validation data be as is. The image below states the correct approach of how the dataset should be resampled in a K-fold fashion.
If you want to achieve this in python, there is a library for that:
Link to the library: https://pypi.org/project/k-fold-imblearn/
I am new to Machine Learning.I am working on a project where the machine learning concept need to be applied.
Problem Statement:
I have large number(say 3000)key words.These need to be classified into seven fixed categories.Each category is having training data(sample keywords).I need to come with a algorithm, when a new keyword is passed to that,it should predict to which category this key word belongs to.
I am not aware of which text classification technique need to applied for this.do we have any tools that can be used.
Please help.
Thanks in advance.
This comes under linear classification. You can use naive-bayes classifier for this. Most of the ml frameworks will have an implementation for naive-bayes. ex: mahout
Yes, I would also suggest to use Naive Bayes, which is more or less the baseline classification algorithm here. On the other hand, there are obviously many other algorithms. Random forests and Support Vector Machines come to mind. See http://machinelearningmastery.com/use-random-forest-testing-179-classifiers-121-datasets/ If you use a standard toolkit, such as Weka, Rapidminer, etc. these algorithms should be available. There is also OpenNLP for Java, which comes with a maximum entropy classifier.
You could use the Word2Vec Word Cosine distance between descriptions of each your category and keywords in the dataset and then simple match each keyword to a category with the closest distance
Alternatively, you could create a training dataset from already matched to category, keywords and use any ML classifier, for example, based on artificial neural networks by using vectors of keywords Cosine distances to each category as an input to your model. But it could require a big quantity of data for training to reach good accuracy. For example, the MNIST dataset contains 70000 of the samples and it allowed me reach 99,62% model's cross validation accuracy with a simple CNN, for another dataset with only 2000 samples I was able reached only about 90% accuracy
There are many classification algorithms. Your example looks to be a text classification problems - some good classifiers to try out would be SVM and naive bayes. For SVM, liblinear and libshorttext classifiers are good options (and have been used in many industrial applcitions):
liblinear: https://www.csie.ntu.edu.tw/~cjlin/liblinear/
libshorttext:https://www.csie.ntu.edu.tw/~cjlin/libshorttext/
They are also included with ML tools such as scikit-learna and WEKA.
With classifiers, it is still some operation to build and validate a pratically useful classifier. One of the challenges is to mix
discrete (boolean and enumerable)
and continuous ('numbers')
predictive variables seamlessly. Some algorithmic preprocessing is generally necessary.
Neural networks do offer the possibility of using both types of variables. However, they require skilled data scientists to yield good results. A straight-forward option is to use an online classifier web service like Insight Classifiers to build and validate a classifier in one go. N-fold cross validation is being used there.
You can represent the presence or absence of each word in a separate column. The outcome variable is desired category.
When given the dataset, normally m instances by n features matrix, how to choose the classifier that is most appropriate for the dataset.
This is just like what algorithm to solve a prime Number. Not every algorithm solve any problem means each problem assigned which finite no. of algorithm. In machine learning you can apply different algorithm on a type of problem.
If matrix contain real numbered features then you can use KNN algorithm can be used. Or if matrix have words as feature then you can use naive bayes classifier which is one of best for text classification. And Machine learning have tons of algorithm you can read them apply to your problem which fits best. Hope you understand what I said.
An interesting but much more general map I found:
http://scikit-learn.org/stable/tutorial/machine_learning_map/
If you have weka, you can use experimenter and choose different algorithms on same data set to evaluate different models.
This project compares many different classifiers on different typical datasets.
If you have no idea, you could use this simple tool auto-weka which will test all the different classifiers you selected within different constraints. Before using auto-weka, you may need to convert your data to ARFF using Weka or just manually (many tutorial on youtube).
The best classifier depends on your data (binary/string/real/tags, patterns, distribution...), what kind of output to predict (binary class / multi-class / evolving classes / a value from regression ?) and the expected performance (time, memory, accuracy). It would also depend on whether you want to update your model frequently or not (ie. if it is a stream, better use an online classifier).
Please note that the best classifier may not be one but an ensemble of different classifiers.
I have 20 attributes and one target feature. All the attributes are binary(present or not present) and the target feature is multinomial(5 classes).
But for each instance, apart from the presence of some attributes, I also have the information that how much effect(scale 1-5) did each present attribute have on the target feature.
How do I make use of this extra information that I have, and build a classification model that helps in better prediction for the test classes.
Why not just use the weights as the features, instead of binary presence indicator? You can code the lack of presence as a 0 on the continuous scale.
EDIT:
The classifier you choose to use will learn optimal weights on the features in training to separate the classes... thus I don't believe there's any better you can do if you do not have access to test weights. Essentially a linear classifier is learning a rule of the form:
c_i = sgn(w . x_i)
You're saying you have access to weights, but without an example of what the data look like, and an explanation of where the weights come from, I'd have to say I don't see how you'd use them (or even why you'd want to---is standard classification with binary features not working well enough?)
This clearly depends on the actual algorithms that you are using.
For decision trees, the information is useless. They are meant to learn which attributes have how much effect.
Similarly, support vector machines will learn the best linear split, so any kind of weight will disappear since the SVM already learns this automatically.
However, if you are doing NN classification, just scale the attributes as desired, to emphasize differences in the influential attributes.
Sorry, you need to look at other algorithms yourself. There are just too many.
Use the knowledge as prior over the weight of features. You can actually compute the posterior estimation out of the data and then have the final model
I am using the explorer feature for classification. My .arff data file has 10 features of numeric and binary values; (only the ID of instances is nominal).I have abt 16 instances. The class to predict is Yes/No.i have used Naive bayes but i cantnot interpret the results,,does anyone know how to interpret results from naive Bayes classification?
Naive Bayes doesn't select any important features. As you mentioned, the result of the training of a Naive Bayes classifier is the mean and variance for every feature. The classification of new samples into 'Yes' or 'No' is based on whether the values of features of the sample match best to the mean and variance of the trained features for either 'Yes' or 'No'.
You could use others algorithms to find the most informative attributes. In that case you might want to use a decision tree classifier, e.g. J48 in WEKA (which is the open-source implementation of C4.5 decision tree algorithm). The first node in the resulting decision tree tells you which feature has the most predictive power.
Even better (as stated by Rushdi Shams in the other post); Weka's Explorer offers purpose build options to find the most useful attributes in a dataset. These options can be found under the Select attributes tab.
As Sicco said NB cannot offer you the best features. Decision tree is a good choice because the branching can sometimes tell you the feature that is important- BUT NOT ALWAYS. In order to handle simple to complex featureset, you can use WEKA's SELECT ATTRIBUTE tab. There, you can find search methods and attribute evaluator. Depending on your task, you can choose the one that best suits you. They will provide you a ranking of the features (either from training data or from a k-fold cross validation). Personally, I believe that decision trees perform poor if your dataset is overfitting. In that case, a ranking of features is the standard way to select best features. Most of the times I use infogain and ranker algorithm. When you see your attributes are ranked from 1 to k, it is really nice to figure out the required features and unnecessary ones.