The aim of this paper is to present the classification problem in data mining using decision trees. Simply stated,data mining refers to extracting or "mining" knowledge from large amounts of data. Data mining known by different names as – knowledge mining, knowledge extraction, data/pattern ...
Classification in data mining involves classifying a set of data instances into predefined classes. Learn more about its types and features with this blog.
Classification, particular Multi-classification problem has been a hot topic in data mining. 分类问题尤其是多类分类问题一直是数据挖掘研究的热点问题。 www.fabiao.net 3. The accuracy of classification of SVM in a two-class classification problem would be decreased because of those promiscuous samples....
In this paper we propose a new modified tree for classification in Data Mining. The proposed modified Tree is inherited from the concept of the decision tree and knapsack problem. A very high dimensional data may be handled with the proposed tree and optimized classes may be generated. 展开 ...
So examining of data as fast as possible is important. In this paper we would be interested to discuss about the data stream mining and the issues of stream classification, like Single scan, Load shedding, Memory Space, Class imbalance problem, Concept drift, and possible ways to solve those...
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 ...
SVM-r is a useful method when data is not linearly separable but slower because of the hyper parameters C and γ optimization problem. For a selection of parameters C and γ, parameter tuning was performed on values C ∈ [20, 21,…, 24] and γ ∈ [2−8, 2−7,…, 1...
In Simoncini et al. (2018), the unbalanced data problem was addressed by investigating the efficiency of advanced NN designs for vehicle multi-class classification by using low-frequency GPS data. Hence, imbalanced datasets are another issue, as they can decrease the capability of learning-based ...
It is, hence, desirable to obtain the "classes" of future prices, which can be cast as an electricity price classification problem. In this paper, we investigate the application and effectiveness of several data mining approaches for electricity market price classification. In addition, we propose...
When non-response makes estimates from a census a small area estimation problem: the case of the survey on graduates’ employment status in Italy Maria Giovanna Ranalli Fulvia Pennoni Antonietta Mira Regular ArticleOpen access10 April 2025