An Overview on Classification Algorithms in Data Mining Data classification is the method of organizing algorithm is a computational procedure which takes some data into categories for its most valuable and effi... V Rengaraj 被引量: 0发表: 0年 加载更多研究点推荐 CLASSIFICATION TECHNIQUES DATA MIN...
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...
Mining Medical Databases with Modified Gini Index Classification Decision tree classification is a common method used in data mining. It has been used for predicting medical diagnoses. Among data mining methods for class... QN Tran - International Conference on Information Technology: New Generations ...
Data mining on clinical data is a challenging area in the field of medical research, aiming at predicting and discovering patterns of disease occurrence and prognosis based on detected symptoms and reported health conditions. Data mining is the process of recovering related, significant and imperative...
This paper presents a review of — and classification scheme for — the literature on the application of data mining techniques for the detection of financial fraud. Although financial fraud detection (FFD) is an emerging topic of great importance, a comprehensive literature review of the subject ...
However this method showed high specificity (Me = 1.0) but low sensitivity (Me = 0.3). Random Forest ranked second in overall accuracy (Me = 0.73) with high area under the ROC (Me = 0.73) specificity (Me = 0.73) and sensitivity (Me = 0.64). Linear Discriminant Analysis also showed ...
Three well known data mining classification algorithms are tested in five different widely used databases to verify the robustness of the proposed method. 展开 关键词: electre i method data mining classification algorithms decision rules DOI: 10.1016/j.cor.2008.12.011 被引量: 63 ...
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 ...
Eager Learning Instance-based learning: lazy evaluation Decision-tree and Bayesian classification: eager evaluation Key differences Lazy method may consider query instance xq when deciding how to generalize beyond the training data D Eager method cannot since they have already chosen global approximation ...
KaiPetersen, inInformation and Software Technology, 2011 4Classification scheme Theclassification schemeis a structure of empirical studies on software productivity measurement and prediction. The scheme consists of six facets, namely quantification approach, abstraction, context, evaluation, research method, ...