## 5 Discussion and Implications  
All classification algorithms studied in Table 7 are sensitive against imbalanced datasets [47]. Results are expected to be biased towards the majority class, making more false negatives. In the case of this study, term deposit subscription, more interested bank clients are expected to be misclassified as uninterested (i.e., higher risk of losing potential opportunity). Results of the experiments in this research support the propositions in [1,40,44]; which expect better results of Meta-Cost over Cost-Sensitive Classification method in some cases of imbalanced datasets. The nature of Meta-Cost method enables it to outperform other related methods in certain cases, this is apparent in the algorithms willingness to make more relevant heuristics. The overall results of using cost-sensitive approaches show classification performance improvement in comparison with classical classification algorithms. Such improvement is illustrated in Figure 6 that plots Type I and Type II errors base lines for Meta-Cost-MLP at 200 Cost Factor as comparison points with other classical classification algorithms. The base lines indicate that Type I and Type II errors are minimized to 0.192 and 0.229, respectively. \begin{table}
\begin{tabular}{l c c c c c c} \hline \hline
**Algorithm** & **TPR** & **TNR** & **Geometric Mean** & **Type I Error** & **Type II Error** & **Accuracy** \\ \hline \multicolumn{6}{l}{**(A) Our approach**} \\ Meta-Cost-MLP & **0.808** & 0.771 & **78.93\%** & **0.192** & 0.229 & 77.48 \\ CostSensitiveClassifier-MLP & 0.614 & 0.872 & 73.17\% & 0.386 & 0.128 & 84.18 \\ \hline \multicolumn{6}{l}{**(B) Conventional machine-learning classifiers**} \\ MLP (Baseline) & 0.39 & 0.95 & 60.87\% & 0.61 & 0.05 & 88.98 \\ DL-MLP & 0.36 & 0.93 & 57.86\% & 0.64 & 0.07 & 86.24 \\ J48 & 0.36 & 0.96 & 58.79\% & 0.64 & 0.04 & 88.9 \\ LL & 0.47 & 0.83 & 62.46\% & 0.53 & 0.17 & 78.61 \\ DT & 0.33 & 0.97 & 56.58\% & 0.67 & 0.03 & 89.49 \\ VFDR & 0.46 & 0.76 & 59.13\% & 0.54 & 0.24 & 72.61 \\ RF & 0.27 & **0.98** & 51.44\% & 0.73 & **0.02** & **89.82** \\ \hline \multicolumn{6}{l}{**(C) Other cost-sensitive results from related works**} \\ CSDE \({}^{*}\)[1] & 0.705 & 0.62.2 & 66.2\% & n.a. & n.a. & n.a. \\ CSDNN \({}^{**}\)[1] & 0.615 & 0.542 & 57.9\% & n.a. & n.a. & n.a. \\ AdaCost [1] & 0.89 & 0.22 & 44.2\% & n.a. & n.a. & n.a. \\ Meta-Cost [1] & 0.35 & 0.868 & 55.1\% & n.a. & n.a. & n.a. \\ \hline \hline \end{tabular}  
* CSDE: Cost-Sensitive Deep Neural Network Ensemble, \({}^{**}\) CSDNN: Cost-Sensitive Deep Neural Network.  
\end{table} 
Table 7: Performance Comparison of Different Classification Algorithms. The comparison of performance asserts that Meta-Cost-based classification maintained an acceptable trade-off between Type I and II errors. Consequently, it would benefit the decision-making process in understanding and judging the probability of clients term deposit subscription. Decision makers in the bank industry would have data-driven assessment of the risks associated with their marketing efforts. Instead of qualitative or probable subjective evaluations of marketing decisions; an objective, quantifiable, and justifiable prospective would support the decisions. Furthermore, market and client segmentation will be based on actual historical data that are derived from the market itself. Calibration of the cost Equation (2) is one of the possible data-driven risk assessment tools. In which \((FN\times\lambda)\) represents the cost not contacting the clients who are willing to apply for a term deposit, and \((FP\times\mu)\) represents the cost of contacting uninterested clients. \[TotalCost=(FN\times\lambda)+(FP\times\mu) \tag{2}\]  
The cost of missing a potential client is much higher than contacting uninterested client. The quantification of the cost for decision makers, according to the best prediction model, is \(TotalCost=(FN\times 200)+(FP\times 1)=(100\times 200)+(918\times 1)=20,918.00\).