*2.8. Sequential Feature Selection*

Sequential feature selection algorithms are a subset of wrapper algorithms that use greedy search algorithms. They evaluate a solution with certain features in a specific model and decide which feature to remove based on its quality. This technique can use a feedforward or backward approach; i.e., adding or removing features in the model. Figure 3 displays the searching schema for feedforward and backward sequential selection with three features [57,58].

**Figure 3.** Flow diagram for feedforward and backward sequential feature selection.

For this study, backward sequential feature selection served to remove the worst variables in the energy consumption dataset for the LED lamps of a CPPS.
