Performance Tuning Of J48 Algorithm For Prediction Of Soil Fertility
This work addresses soil fertility classification for agricultural applications, but it is incremental as it focuses on tuning an existing method.
The paper tackled soil fertility prediction by applying decision tree algorithms to agricultural soil datasets, achieving improved performance through tuning of the J48 algorithm with attribute selection and boosting techniques.
Data mining involves the systematic analysis of large data sets, and data mining in agricultural soil datasets is exciting and modern research area. The productive capacity of a soil depends on soil fertility. Achieving and maintaining appropriate levels of soil fertility, is of utmost importance if agricultural land is to remain capable of nourishing crop production. In this research, Steps for building a predictive model of soil fertility have been explained. This paper aims at predicting soil fertility class using decision tree algorithms in data mining . Further, it focuses on performance tuning of J48 decision tree algorithm with the help of meta-techniques such as attribute selection and boosting.