Optimal Feature Subset Selection for Uncertainty Classification: Precision Agriculture

Uncertainty Classification, Optimal Feature Subset Selection, and Sequential problems are some of the critical problems in machine learning. The optimal feature subset in uncertainty classification plays a critical role. The main problem of classification is the handling of ambiguous data. Various classification methods decrease generalization performance if the ambiguity is present in the input features itself. This paper addresses the above problems by designing an Incremental Fuzzy Extreme Learning Machine algorithm (I-FELM) for batch and an Incremental Fuzzy Online Sequential Extreme Learning Machine algorithm (IF-OSFELM) for sequential input. The optimal feature subset is selected by using seven feature subset selection methods - F-Score, Students’ T-test, Kullback-Leibler divergence, Kolmogorov-Smirnov test, Information Gain, Symmetric uncertainty, and Gain Ratio. For fuzzification, the trapezoidal membership function is used. The experimental results are calculated for the proposed algorithms using the clinical data set. In order to check the efficiency of the proposed algorithms, two Precision Agriculture expert systems are developed: 1. IoT-based plant disease classification and 2. ML-based Crop Yield Prediction that helps the remote farmer with expert advice, in which the proposed algorithms are exploited. Statistical approaches are used for validation and hypothetical testing.