The forest coverType Dataset Contains the following attributes distributed in many boolean features.
Wilderness_Area (4 binary columns) / qualitative / 0 (absence) or 1 (presence) / Wilderness area designation Soil_Type (40 binary columns) / qualitative / 0 (absence) or 1 (presence) / Soil Type designation
Training the SVC classifier with RBF kernel without attributes re-engineering takes too much time. Can we merge all 40 binary columns for form a Soil_Type Attribute representing those columns. What are the pros and cons of this approach ?
Also, do we have any direct approach to achieve this in weka or sklearn ?