The Infona portal uses cookies, i.e. strings of text saved by a browser on the user's device. The portal can access those files and use them to remember the user's data, such as their chosen settings (screen view, interface language, etc.), or their login data. By using the Infona portal the user accepts automatic saving and using this information for portal operation purposes. More information on the subject can be found in the Privacy Policy and Terms of Service. By closing this window the user confirms that they have read the information on cookie usage, and they accept the privacy policy and the way cookies are used by the portal. You can change the cookie settings in your browser.
Classification of malicious code by machine learning gives more flexible and adaptable prediction result than by existing approaches [1]. But the approach just can identify looks-like malicious code instead of real malicious one. In this research, a novel method to reduce the vagueness in the classification by machine learning to consider code sequence.
Feature selection (FS) methods play two important roles in the context of neuroimaging based classification: potentially increase classification accuracy by eliminating irrelevant features from the model and facilitate interpretation by identifying sets of meaningful features that best discriminate the classes. Although the development of FS techniques specifically tuned for neuroimaging data is an...
An active under-sampling approach is proposed for handling the imbalanced problem in this paper. Traditional classifiers usually assume that training examples are evenly distributed among different classes, so they are often biased to the majority class and tend to ignore the minority class. in this case, it is important to select the suitable training dataset for learning from imbalanced data. the...
Mining data streams has attracted the attention of the scientific community in recent years with the development of new algorithms for processing and sorting data in this area. Incremental learning techniques have been used extensively in these issues. A major challenge posed by data streams is that their underlying concepts can change over time. This research delves into the study of applying different...
The link prediction problem in social networks defined as a task to predict whether a link between two particular nodes will appear in the future is still a broadly researched topic in the field of social network analysis. However, another relevant problem is solved in the paper instead of individual link forecasting: prediction of key network measures values, what is a more time saving approach....
Identifying the subject's simple judging states from fMRI data is the basis of studying complex logical relationship and has great theoretical significance. In this paper, we study judging states from fMRI data in terms of logical recognition classifications. We found that the ROI (Regions of Interest) regions played an important role in visual recognition task and identified what ROI regions were...
Discretization algorithms have played an important role in many areas such as artificial intelligence, data mining and machine learning. In this paper, we propose an effective bottom-up discretization algorithm, namely EBDA. Firstly, we present a novel merging criterion which not only considers the effect of variance on degrees of freedom in the two merged intervals but also the effect of variance...
In this paper, feature selection was carried out for multi-intelligence classification, and finds key regions. We designed different multi-intelligence tasks with BCI. SVM was used to classify and select features. The experiment reveals that a band has a greater effect on imagery intelligent tasks. And the introduced feature selection algorithm succeeded to detect key regions for multi-intelligence...
Set the date range to filter the displayed results. You can set a starting date, ending date or both. You can enter the dates manually or choose them from the calendar.