Classification Methods in Data Mining - Explore various classification methods in data mining, including decision trees, neural networks, and support vector machines. Learn how these techniques can enhance your data analysis.
For 500 test data, the similarity was 96%. It is proven that this method has a high accuracy so that it can help the decision makers solved the company problem in predicting the types of motorcycle parts to be restocked.S L B Ginting...
After that data filtering is needed, for example, removing ‘0’ values (in the image data are empty values), outlier detection, trait reproducibility assessment. For outlier detection, Grubbs test24 is a useful method based on assumption of the normal distribution of phenotype data points for ...
The random forests (RF) method constructs an ensemble of tree predictors, where each tree is constructed on a subset randomly selected from the training data, with the same sampling distribution for all trees in the forest (Breiman, 2001). Random forest is a popular nonparametric tree-based ens...
9 For risk prediction of heart disease, various data mining techniques are applied in the research paper.10 Analysis of lung cancer is presented by applying different mining techniques by the authors of.11 Authors of,12 suggested a method to increase the prediction accuracy of kidney disease. ...
Furthermore, this method is quite user-friendly since it has only two parameters that the user needs to define: the number of random trees in the forest; and the number of predictor variables in the random subset of tree at each node. These parameters can be easily optimized although random...
Time Series Classification (TSC) is an important and challenging problem in data mining. With the increase of time series data availability, hundreds of TS
SMOTE method A SMOTE is a sampling technique. It randomly creates additional minority class occurrences from the pattern’s minority class neighbors. These individuals are constructed using features from the initial data to complete actual minority class samples. The SMOTE approach is used in the prop...
in this case, an extended version of the internet movie database (IMDb) dataset with 50,000 samples. The training sample size provided to each method is increased from 400 to 40,000 while monitoring the CNN accuracy and the hybrid accuracy. For choosing the right data representation from the...
Data envelopment analysis:Data envelopment analysis (DEA) is able to handle multiple inputs and outputs. The inputs and outputs are weighted and their ratio (productivity) should be maximized for each decision making unit. The weights are determined by the method. Data envelopment analysis determine...