that is, a feature vector reflecting occurrences of words in the page is constructed. Usually, there are very few instances of the interesting category in text categorization. This overrepresentation of the negative class in information retrieval problems can cause problems in evaluating classifiers' performances. Since error rate is not a good metric for skewed datasets, the classification performance of algorithms in information retrieval is usually measured by *precision* and *recall*:

$$r e c a l l={\frac{T P}{T P+F N}}$$

![0_image_0.png](0_image_0.png)

$$p r e c i s i o n={\frac{T P}{T P+F P}}$$

![0_image_1.png](0_image_1.png)

Mladeni´c and Grobelnik (1999) proposed a feature subset selection approach to deal with imbalanced class distribution in the IR domain. They experimented with various feature selection methods, and found that the *odds ratio* **(van Rijsbergen, Harper, & Porter,**
1981) when combined with a Naive Bayes classifier performs best in their domain. Odds ratio is a probabilistic measure used to rank documents according **to their relevance to the**
positive class (minority class). *Information gain* **for a word, on the other hand, does not** pay attention to a particular target class; it is computed per word for each class. In an imbalanced text dataset (assuming 98 to 99% is the negative class), most of the features will be associated with the negative class. *Odds ratio* **incorporates the target class information in** its metric giving better results when compared to *information gain* **for text categorization.**
Provost and Fawcett (1997) introduced the ROC convex hull method to estimate the classifier performance for imbalanced datasets. They note that the problems of unequal class distribution and unequal error costs are related and that little work has been done to address either problem (Provost & Fawcett, 2001). In the ROC **convex hull method, the**
ROC space is used to separate classification performance from the class and cost distribution information.

To summarize the literature, under-sampling the majority class enables better classifiers to be built than over-sampling the minority class. A combination of the two as done in previous work does not lead to classifiers that outperform those built utilizing only undersampling. However, the over-sampling of the minority class **has been done by sampling with**
replacement from the original data. Our approach uses a different method of over-sampling.

## 4. Smote: Synthetic Minority Over-Sampling Technique 4.1 Minority Over-Sampling With Replacement

Previous research (Ling & Li, 1998; Japkowicz, 2000) has discussed over-sampling with replacement and has noted that it doesn't significantly improve minority class recognition. We interpret the underlying effect in terms of decision regions in feature space. Essentially, as the minority class is over-sampled by increasing amounts, the effect is to identify similar but more specific regions in the feature space as the decision **region for the minority class.** This effect for decision trees can be understood from the plots in Figure 3.