A Novel Neuro-fuzzy Classification Technique for Soil Data Mining

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Data mining with agricultural soil databases is a relatively young research area. In agricultural field, the determination of soil category mainly depends on the atmospheric conditions and different soil characteristics. Classification as an essential data mining technique used to develop models describing different soil classes. Such analysis can present us with a complete understanding of various soil databases at large. In our study, we proposed a novel Neuro-fuzzy classification based technique and applied it to large soil databases to find out significant relationships. We used our technique to three benchmark data sets from the UCI machine learning repository for soil categorization and they were namely Statlog (Landsat Satellite), Covertype, and 3 data sets. Our objective was to develop an efficient classification model with the proposed method and, therefore compare its performance with two well-known supervised classification algorithms Multilayer Perceptron and Support Vector Machine. We estimated the performance of these classification techniques in terms of different evaluation measures like Accuracy, Kappa statistic, True-Positive Rate, False-Positive Rate, Precision, Recall, and F-Measure. The proposed technique had an accuracy of 99.4 % with the Statlog data set, 97.7 % with the Covertype data set and 90 % with the 3 data set; and in every aspect, it performed better than Multilayer Perceptron and Support Vector Machine algorithms.

Data mining consists of extracting interesting patterns representing knowledge from real-world databases. The software applications related with data mining includes various methodologies developed by both commercial and research organizations. Different data mining techniques used to...

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...It combines the human alike logical reasoning of fuzzy based systems with the learning and connectedness structure of ANNs by means of the fuzzy sets and linguistic model based approaches. In our work, we proposed a novel Neuro-fuzzy based classification method for soil data mining. We applied our method to three benchmark data sets from the UCI machine learning repository for soil classification and, therefore compare its performance with MLP and SVM based classification models.
This research study is arranged as follows: Section 2 includes the related works done in this field; Section 3 describes our proposed Neuro-fuzzy classification based method. Section 4 explains the methodology in terms of our proposed neuro-fuzzy method, MLP, and SVM. Section 5 discusses the classification performance analysis and results; and the Section 6 is reserved for the conclusion.

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