## 3 Background
### Dataset of Spanish Companies  
This dataset was for Spanish companies, from which we considered several financial and non-financial features. We considered the dependent variable of bankruptcy as the class for each record or sample and we aimed to classify the instances according to class. The dependent variable was insolvency, which corresponded to the existence of continued losses over three years [33].  
This dataset was extracted from the Infotel database (which was bought from [[http://infotel.es](http://infotel.es)]([http://infotel.es](http://infotel.es)), accessed on 1 May 2017). As a result, we had data from 470 businesses that were gathered over six years (from 1998 to 2003). There were 2860 samples in all, with 62 corresponding to insolvent companies, meaning that insolvency cases only formed 2% of the whole sample.  
Initially, each row of the dataset had 37 independent variables and 1 dependent variable (bankruptcy). A prior effort by the authors in [33] changed this list by removing unnecessary variables (i.e., those without significance, for instance, internal database firm codes), resulting in 33 independent variables. So, every record in the dataset that was used in this work had 33 features, which comprised a mix of financial indicators and non-financial indicators. Each feature had either a qualitative (categorical) or a quantitative (numerical) value. Table 1 shows the independent variables after removing the unnecessary variables, as well as their type and description. The size of the firm, the kind of company, provincial code (i.e., where the company is situated), and the auditor's judgments were among the non-financial data that had categorical value. Usually, the size of the firm is a number, but in this dataset, it was either small, medium or large, based on the size of the company. Moreover, in this work, we used all 33 features without applying feature selection because, as pointed out by [34], adding a feature selection step would not improve the results.  
\begin{table}
\begin{tabular}{c c c} \hline
**Financial Variables** & **Description** & **Type** \\ \hline Debt Structure & Long-term Liabilities/Current Liabilities & Real \\ Debt Amount & Interest Amount/Total Liabilities & Real \\ Debt-Paying Ability & Operating Cash Flow/Total Liabilities & Real \\ Debt Ratio & Total Assets/Total Liabilities & Real \\ Working Capital & Working Capital/Total Assets & Real \\ Warranty & Financial Warranties & Real \\ Operating Income Margin & Operating Income/Net Sales & Real \\ Returns on Operating Assets & Operating Income/Average Operating & Real \\ Returns on Equity & Net Income/Average Total Equity & Real \\ Returns on Assets & Net Income/Average Total Assets & Real \\ Stock Turnover & Cost of Sales/Average Inventory & Real \\ Asset Turnover & Net Sales/Average Total Assets & Real \\ Receivables Turnover & Net Sales/Average Receivables & Real \\ Asset Rotation & Asset Allocation Decisions & Real \\ Financial Solvency & Current Assets/Current Liabilities & Real \\ Acid Test & (Cash Equivalents + Marketable & \\  & Securities & \\  & + Net Receivables)/Current Liabilities & Real \\ \hline
**Non-Financial Variables** & **Description** & **Type** \\ \hline Year & Corresponding to the sample & Integer \\ Size & Small, medium or large & Categorical \\ Number of Employees & & Integer \\ Age of Company & & Integer \\ Type of Company & Public company, limited liability & Categorical \\  & company or other & \\ \hline \end{tabular}
\end{table}
Table 1: The independent variables of the dataset of Spanish companies (financial and non-financial).