Data Analysis for Fuzzy Extreme Learning Machine

Fuzzy extreme learning machine (F-ELM) one of the learning algorithm which is specifically used for uncertainty classification. Uncertainty classification is critical problem in the area of machine learning. In various real-time applications, the ambiguity is present in the input dataset itself which affects the systems generalization performance. Data (feature) analysis plays a major role in such types of problems. To solve the said problem in this paper, distance-based Relief algorithm and fuzzy extreme learning algorithm are used for data analysis and classification, respectively which contributes to Relief-based data analysis for F-ELM (RFELM++) and Relief-based data analysis for online sequential ELM (RFOSELM++) for batch mode and sequential input, respectively. Experimental results are calculated by using clinical dataset. Through the results, it is observed that RFELM++ produces increased accuracy in comparison with RELM++ for clinical dataset. The RFOSELM++ maintain accuracy by using 41.5% features as differentiate to OS-ELM for the UCI Repository dataset. The proposed RFOSELM++ algorithm is compared with similar already available sequential based algorithms. As a case study, a novel application of classification of nutrient deficiency and plant disease in which ambiguity presents is considered. Both proposed algorithms are exploited in this expert system which helps the remote farmer with expert advice. The proposed RFELM++ algorithm is tested and validated by using statistical methods.