Knowledge extraction of agricultural data using artificial immune system

Abstract

Mining agricultural data with artificial immune system (AIS) algorithms, particularly the clonal selection algorithm (CLONALG) and artificial immune recognition system (AIRS) form the bedrock of this paper. A fuzzy-rough feature selection (FRFS) method is coupled with CLONALG and AIRS for improved detection and computational efficiency. Comparative simulations with sequential minimal optimization and multi-layer perceptron reveal that the CLONALG and AIRS produced significant results. Their respective FRFS upgrades namely; FRFS - CLONALG and FRFS - AIRS are able to generate highest detection rates and lowest false alarm rates. Thus, gathering useful information with the AIS models can help to enhance productivity related to agriculture.