The model is intended to be used for predicting whether a graduate was employed, unemployed, or in an undetermined situation.Bangsuk JantawanCheng-Fa TsaiEprint ArxivJantawan, Bangsuk, and Cheng-Fa Tsai. "The Application of Data Mining to Build Classification Model for Predicting Graduate ...
Data mining techniques play an important role in data analysis. The application of data mining to educational data allows the educators to discover useful knowledge about students. This paper presents a classification model based on decision... S Joseph,L Devadas 被引量: 3发表: 2015年 Concept ...
Successful application of these techniques will be valuable input for decision-making during preventive maintenance scheduling. 展开 关键词: Shovels failures classification clustering data mining reliability support vector machine DOI: 10.1080/17480930.2015.1123599 年份: 2016 ...
Label your data: The quality of data labeling is a key factor in determining model performance. Documents that belong to the same class should always have the same class, if you have a document that can fall into two classes use Multi label classification projects. Avoid class ambiguity, mak...
The purpose of this report consisting of path coordinates of the mice in the arena is to describe a novel application of a data mining algorithm (Figure 1, left) under the effect of drugs from the three named Pattern Array (PA; see Kafkafi et al, in press) to classes (Table 1), ...
Similarly, it can be used in classifying the state of a machine into faulty or good. Other application areas where binary classification can be used are medical diagnosis, spam detection, etc. 2. Multiclass classification: As the name suggests, multiclass classification has more than two ...
The Echo state network (ESN) is an efficient recurrent neural network that has achieved good results in time series prediction tasks. Still, its application in time series classification tasks has yet to develop fully. In this study, we work on the time series classification problem based on ec...
Classification: The classification algorithms use supervised, semi-supervised, and deep learning models in order to classify the input data streams. The classifiers use single class recognition or multi class prediction models depending upon the application requirements. The classification algorithms vary in...
Time Series Classification (TSC) is an important and challenging problem in data mining. With the increase of time series data availability, hundreds of TSC algorithms have been proposed. Among these methods, only a few have considered Deep Neural Networks (DNNs) to perform this task. This is ...
Product: Product attributes are often used as output measure in the form of size and quality. To compare size one has to be aware of the type of the product (e.g. when counting features or functions these are different for an end-user application or an embedded application interacting with...