# Impact of Imbalanced Datasets Preprocessing in the Performance of Associative Classifiers
## 3 Materials and Methods
### Datasets  
This section describes the datasets that will be used to assess the impact of preprocessing of financial data in the performance of associative classifiers. Some of these datasets are well known in the literature, in addition to being a reference, because they are widely used in many of the research work carried out so far. The used datasets are: _Australian Credit Approval_ (Australian) ([[https://archive.ics.uci.edu/ml/datasets/Statlog+](https://archive.ics.uci.edu/ml/datasets/Statlog+)]([https://archive.ics.uci.edu/ml/datasets/Statlog+](https://archive.ics.uci.edu/ml/datasets/Statlog+))(Australian+Credit+Approval)), _German Credit data_ (German) ([[https://archive.ics.uci.edu/ml/datasets/Statlog+](https://archive.ics.uci.edu/ml/datasets/Statlog+)]([https://archive.ics.uci.edu/ml/datasets/Statlog+](https://archive.ics.uci.edu/ml/datasets/Statlog+))(German+Credit+Data)), _Japanese Credit Approval_ (Japanese) ([[https://archive.ics.uci.edu/ml/datasets/Credit+Approval](https://archive.ics.uci.edu/ml/datasets/Credit+Approval)]([https://archive.ics.uci.edu/ml/datasets/Credit+Approval](https://archive.ics.uci.edu/ml/datasets/Credit+Approval))), _Default of credit card clients_ (Default credit) ([[https://archive.ics.uci.edu/ml/datasets/default+of+credit+card+clients](https://archive.ics.uci.edu/ml/datasets/default+of+credit+card+clients)]([https://archive.ics.uci.edu/ml/datasets/default+of+credit+card+clients](https://archive.ics.uci.edu/ml/datasets/default+of+credit+card+clients))), _Iranian_ (Shared personally by [PERSON]), _Polish bankruptcy_ (Polish_1 to Polish_5) ([[https://archive.ics.uci.edu/ml/datasets/Polish+companies+bankruptcy+data](https://archive.ics.uci.edu/ml/datasets/Polish+companies+bankruptcy+data)]([https://archive.ics.uci.edu/ml/datasets/Polish+companies+bankruptcy+data](https://archive.ics.uci.edu/ml/datasets/Polish+companies+bankruptcy+data))), _The PAKDD 2009 dataset_ (The PAKDD) ([[https://www.cs.purdue.edu/commugrate/data/credit_card/](https://www.cs.purdue.edu/commugrate/data/credit_card/)]([https://www.cs.purdue.edu/commugrate/data/credit_card/](https://www.cs.purdue.edu/commugrate/data/credit_card/))), _Give me some credit_ (Give me credit) ([[https://www.kaggle.com/c/GiveMeSomeCredit/data](https://www.kaggle.com/c/GiveMeSomeCredit/data)]([https://www.kaggle.com/c/GiveMeSomeCredit/data](https://www.kaggle.com/c/GiveMeSomeCredit/data))) and _Qualitative bankruptcy data_ (Qualitative) ([[https://archive.ics.uci.edu/ml/datasets/qualitative_bankruptcy](https://archive.ics.uci.edu/ml/datasets/qualitative_bankruptcy)]([https://archive.ics.uci.edu/ml/datasets/qualitative_bankruptcy](https://archive.ics.uci.edu/ml/datasets/qualitative_bankruptcy))). These datasets are very interesting, due to the fact that they include both numeric and categorical attributes, and missing data. As a summary, Table 1 shows a description of the datasets used in this investigation. The abbreviations (Num.) and (Cat.) refer to the number of numerical and categorical attributes, respectively. IR represents the imbalance ratio of the datasets. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Dataset**} & \multirow{2}{*}{**Instances**} & \multicolumn{2}{c}{**Attributes**} & \multirow{2}{*}{**Missing**} & \multirow{2}{*}{**IR**} \\ \cline{3-3} \cline{5-6}  & & **Num.** & & **Cat.** \\ \hline Australian & 690 & 8 & 6 & No & 1.25 \\ Default credit & 30,000 & 13 & 10 & No & 3.52 \\ German & 1000 & 7 & 13 & No & 2.33 \\ Give me credit & 150,000 & 10 & 0 & No & 13.96 \\ Iranian & 1002 & 28 & 0 & Yes & 19.04 \\ Japanese & 690 & 6 & 9 & Yes & 1.21 \\ Polish\_1 & 7027 & 64 & 0 & Yes & 24.93 \\ Polish\_2 & 10,173 & 64 & 0 & Yes & 24.43 \\ Polish\_3 & 10,503 & 64 & 0 & Yes & 20.22 \\ Polish\_4 & 9792 & 64 & 0 & Yes & 18.01 \\ Polish\_5 & 5910 & 64 & 0 & Yes & 13.41 \\ Qualitative & 250 & 0 & 6 & No & 1.34 \\ The PAKDD & 20,000 & 10 & 9 & Yes & 4.12 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Description of the datasets.