Classification Performance of the IDS Method Based on the Two-Spiral Benchmark
Masayuki Murakami, Nakaji Honda · 2006
The ink drop spread (IDS) method is a modeling technique used in the active learning method (ALM), which is a new approach to soft computing. It is characterized by a modeling process which is based on computing that uses intuitive pattern information instead of complex formulas. It has been proved that the IDS method is capable of stable fast modeling for complex nonlinear targets. In this paper, the classification performance of the IDS method is investigated. The two-spiral problem is a popular classification benchmark, and it is difficult to achieve the perfect classification due to high nonlinearity. With regard to this benchmark the IDS method exhibited good performance in terms of the classification rate and learning speed. This paper also present two learning modes, one of which is effective in solving the two-spiral problem rapidly.