In Machine Learning, many of us come across problems like anomaly detection in which classes are highly imbalanced. Like, for binary classification (0 and 1 class) more than 85% of data points belong to either class.
So, in this blog will cover techniques to handle highly imbalanced data. Some of the classic examples are fraud detection, anomaly detection etc.
Let’s dive into the details of these techniques.
List of techniques -:
- Random Undersamopling
- Random Oversampling
- Python imblearn Undersampling
- Python imblearn Oversampling
- Oversampling : SMOTE(Synthetic Minority Oversampling Technique)
- Undersampling: Tomek Links
Looking at imbalanced data
Here, I have collected raw data from here -:
Data is about classification of glass. It has 2 classes which are imbalanced in nature, here it is not highly imbalanced but they are imbalanced. Let’s take a look with the help of code.
So, here positive class covers about 15% of data which is imbalance in nature
Now, let’s start looking at techniques to handle this problem.
In this, what will happen is the majority class examples will be under sampledi.e., some of the examples which belong to majority class will be removed. It has advantages but it may cause a lot of information loss in some of the cases. Let’s look at code, how to perform undersampling in Python Django development.
count_class_n, count_class_p=data.Class.value_counts() # Divide by class data_class_n=data[data['Class'] ==' negative'] data_class_p=data[data['Class'] ==' positive'] ## Undersampling over negative class(data_class_n) data_class_n_under=data_class_n.sample(count_class_p) ## Concatenating undersamples classes of Negative(undersampled) with the minority class for final data set data_under=pd.concat([data_class_n_under, data_class_p], axis=0) ## Checking the counts of each class after undersampling print('Class count after under-sampling:') print(data_under.Class.value_counts()) ## Plotting the graphs data_under.Class.value_counts().plot(kind='bar', title='Count of Classes') Class count after under-sampling: negative 33 Positive 33 Name: Class, dtype: int64
After undersampling, we have 33 data points in each class.
Here undersampling is not a better option because we already have 200 points and after that we reducing just to 30’s which is very less. So, it depends upon the usecase as well.
In random oversampling, it will create the duplicates of randomly selected examples in minority class.
## Oversampling over positive class(data_class_p) data_class_p_over=data_class_p.sample(count_class_n, replace=True) ## Concatenating undersamples classes of Positive(Oversampled) with the majority class for final data set data_over=pd.concat([data_class_p_over, data_class_n], axis=0) ## Checking the counts of each class after oversampling print('Class count after under-sampling:') print(data_over.Class.value_counts()) ## Plotting the graphs data_over.Class.value_counts().plot(kind='bar', title='Count of Classes', color=['b','r']) Class count after under-sampling: positive 181 negative 181 Name: Class, dtype: int64
Undersampling using Imblearn
Let’s look at undersampling using imblearn package in Python.
Similarly we can perform oversampling using Imblearn. It might confuse you why to use different libraries of performing undersampling and oversampling. Sometimes, it happens that undersampling(oversampling) using imblearn and simple resampling produce different results and we can select based on the performance as well.
importimblearn fromimblearn.under_samplingimportRandomUnderSampler Undersample=RandomUnderSampler(return_indices=True) ## preparing the data in the format X = data[['RI_real', 'Na_real', 'Mg_real', 'Al_real', 'Si_real', 'K_real', 'Ca_real', 'Ba_real', 'Fe_real']] y =data['Class'] ## Fitting for performing undersampling using imblearn X_rus, y_rus, id_rus=Undersample.fit_sample(X, y) print("Count of class after undersampling") print(pd.Series(y_rus).value_counts()) ## Plotting the graph for class counts after undersampling imblearn pd.Series(y_rus).value_counts().plot(kind='bar', title='Count of Classes', color=['r','b']) Count of class after undersampling negative 33 positive 33 dtype: int64
Tomek links is the algorithm based on the distance criteria, instead of removing the data points randomly. It uses distance metric to remove the points from majority class. It finds the pair of points which has less distance between them one from minority class and another from majority class and will remove the majority point from that pair.
fromimblearn.under_samplingimportTomekLinks ## Creating the Tomeklink object tl=TomekLinks(return_indices=True, ratio='majority') ## Fitting on data X_tl, y_tl, id_tl=tl.fit_sample(X, y) print("Count of class after Tomek-links undersampling") print(pd.Series(y_tl).value_counts()) ## Plotting the class count after the undersampling(Tomeklinks) pd.Series(y_tl).value_counts().plot(kind='bar', title='Count of Classes', color= ['r','b']) Count of class after Tomek-links undersampling negative 172 positive 33 dtype: int64
It stands from synthetic minority oversampling technique. It also performs oversampling.
fromimblearn.over_samplingimport SMOTE ## Performing the Smote oversampling smote= SMOTE(ratio='minority') X_sm, y_sm=smote.fit_sample(X, y) ## Checking and Plotting the points after smote oversampling print("Count of class after SMOTE undersampling") print(pd.Series(y_sm).value_counts()) pd.Series(y_sm).value_counts().plot(kind='bar', title='Count of Classes', color = ['b','r']) Count of class after undersampling positive 181 negative 181 dtype: int64
Hope you are clear with different techniques to overcome with imbalanced dataset in Machine Learning. There are many other methods to deal with imbalance thing. It depends upon your dataset when to perform oversampling and when to perform undersampling. If your dataset is immensely large then go for undersampling otherwise perform oversampling or TomekLinks.
Hope you enjoyed learning. Keep learning with new stuff in Machine Learning