首页|New Machine Learning Findings from Karpagam College of Engineering Outlined (Eml arde Tree: Ensemble Machine Learning Based Random De-correlated Extra Decision T ree for the Forest Cover Type Prediction)
New Machine Learning Findings from Karpagam College of Engineering Outlined (Eml arde Tree: Ensemble Machine Learning Based Random De-correlated Extra Decision T ree for the Forest Cover Type Prediction)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – A new study on Machine Learning is now available. According to news originating from Coimbatore, India, by NewsRx corr espondents, research stated, “Forest cover type prediction is used for the fores t management organizations. It also get the insight on area of the forest cover up to date and development lack in present time.” Our news journalists obtained a quote from the research from the Karpagam Colleg e of Engineering, “Classification of the forest area and type of trees could eve ntually help in maintaining the eco system and to get inference on deforestation . In present scenario this problem gains more attention hence to retain the clim ate change impact forest cover type and area prediction would help a lot. This p aper proposes a novel ensemble machine learning based random de-correlated extra decision tree model for the forest cover type prediction. The tree based classi fiers perform well in prediction of the forest cover data. Many researchers use tree based classifiers for the problem. Even though the enhancement of the accur acy seems to be lower in the multi-class classification problem. So, this resear ch proposes the Extra random de-correlated decision tree method for the predicti on of the forest cover. The results the multiple de-correlated decision trees ar e aggregated for the final classification. This proposed method is the ensemble based method. In ensemble machine learning method combines several base optimal results in order to produce one final optimal result. A decision tree follows a simple predictive outcomes based on the series of the cause and effect values. W hile adopting the decision tree models the user has to follow the factors includ ing the variable on which the decision to be taken and threshold for deciding th e class. Instead of depending on one tree for decision making, multiple tree spl it criteria can be considered. Also these ensemble based machine learning allow to fine tune the predictor variable based on the feature to use and split criter ia. The random forest based methods follows the bagging strategy. It has a major role in the split aspect and decision-making aspect in significant manner. This machine learning model decides where to split based on random selection of feat ures. Random forest tree methods have a uniqueness where each split can be done through scrutiny of different features. This paper proposes the ensemble machine learning based random de-correlated extra decision tree model for the forest co ver type prediction. This algorithm especially suits the problem for the multicl ass classification nature. Forest cover type prediction helps in identifying the wilderness type and total area of the forest predicted and available. The datas et considered for the paper is from the UCI Machine Learning repository. It cont ains various features including elevation, slop, aspect, vertical and horizontal distance to hydrology, fire points and roadways, hill shade, wilderness area, s oil type and cover type. Initially the preprocessing is done in the data set by identifying the missing values, outlier detection and formatting data. Later the exploratory analysis is carried out using the Pearson correlation coefficients aspect. Then three machine learning techniques: Multiclass SVM, Boosting and pro posed EMLARDE were deployed. The accuracy of the proposed EMLARDE method outperf orms the other two algorithms.”
CoimbatoreIndiaAsiaCyborgsEmergi ng TechnologiesMachine LearningKarpagam College of Engineering
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