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 k
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 TS
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...
Thus, the above minimization is generally solved through a dual formulation problem [see e.g. [41, 43]]: MathML subjected to the linear constrains MathML Where α i (i = 1,...,n) are nonnegative Lagrange multipliers and K(.) is a kernel unction. In classification problems (c-SVM)...
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....
The classification problem is closely related to the clustering problem discussed in Chaps. 6 and 7. While the clustering problem is that of determining similar groups of data points, the classification problem is that of learning the structure of a data
Surprisingly, a really miserable classifier can achieve an even lower classification accuracy. For example, in a medical problem of breast cancer prognosis, the majorityclass has an 80% prevalence. Many classifiers achieve the classification accuracy lower than 80%. The reason for this anomaly is th...
ReferencesMethods usedProblem highlightedAchievementLimitations Darwazeh et al. (2015) Data classification and security Increased data in cloud computing with less security The cloud model mitigates latency and processing time required to secure data using various security methods and different key sizes to...
In this article, we investigate the effects of several over-and undersampling, cost-sensitive learning and boosting techniques on the problem of learning from imbalanced behaviour data. Oversampling techniques show a good overall performance and do not seem to suffer from overfitting as traditional ...