PDF Publication Title:
Text from PDF Page: 138
Considering all metrics, F1 can be considered the most important metric in this scenario. F1 is the harmonic mean of precision and recall and provides a better indication of prediction performance for unbalanced datasets. F1 gets its best value at 1 and worst at 0. When doing either classification or feature reconstruction we will face a multi-class classification problem. There are two possible ways to give results in this case: aggregated and One-vs.-Rest results. For One-vs.-Rest, we focus in a particular class (label) and consider the other classes as a single alternative class, simplifying the problem to a binary classification task for each particular class (one by one). In the case of aggregated results, we try to give a summary result for all classes. There are different alternatives to perform the aggregation (micro, macro, samples, weighted), varying in the way the averaging process is done [27]. Considering the results presented in this paper, we have used the weighted average provided by scikit-learn [27], to calculate the aggregated F1, precision and recall scores. 4.1. Classification We can use ID-CVAE as a classifier. Figure 4 shows the process necessary to perform classification. The process consists of two phases (in order): training and prediction phase. Figure 4. Classification framework. In the training phase, we train the model using a training dataset together with its associated labels. We train the model as presented in Section 3, trying to minimize the Μ difference between the recovered and ground truth training dataset: πΏπ‘ππππ vs. πΏπ‘ππππ. In the prediction phase the objective is to retrieve the predicted labels for a new test dataset: πΏπ‘ππ π‘. The prediction phase is made up of two steps (Figure 4). In the first step we apply the previously trained model to perform a forward pass to obtain a recovered test dataset Doctoral Thesis: Novel applications of Machine Learning to NTAP - 136PDF Image | Novel applications of Machine Learning to Network Traffic Analysis
PDF Search Title:
Novel applications of Machine Learning to Network Traffic AnalysisOriginal File Name Searched:
456453_1175348.pdfDIY PDF Search: Google It | Yahoo | Bing
Cruise Ship Reviews | Luxury Resort | Jet | Yacht | and Travel Tech More Info
Cruising Review Topics and Articles More Info
Software based on Filemaker for the travel industry More Info
The Burgenstock Resort: Reviews on CruisingReview website... More Info
Resort Reviews: World Class resorts... More Info
The Riffelalp Resort: Reviews on CruisingReview website... More Info
| CONTACT TEL: 608-238-6001 Email: greg@cruisingreview.com | RSS | AMP |