Criterion 3: 푆푝푒푐푖푓푖푐푖푡y is considered as negative integer value. 푆푝푒푐푖푓푖푐i푡y indicates the model’s ability for recognizing natural examples. If this criterion and sensitivity increase and the difference is less than 1%, the best classifier is provided. The criterion’s equation is:
Identifying the decision criteria and allocating weights to the criteria are components of the specific plans within the planning process. Identifying what is relevant to the purchase and giving each priority the correct weight helped the plan to come to fruition. Every element of the process must be specified so that the best decision will be made.
Nowadays, there exist several different forms of media that help keep society informed and updated with the current events. Ronal Deibert and Rafal Rohozinski write about a relatively new but emerging form in “Liberation vs. Control: The Future of Cyberspace”. Whereas, Brent Cunningham addresses a more traditional form in his “Re-thinking Objectivity: Columbia Journalism Review”. In each piece, the authors analyze the process that information is composed and presented to the audience. While Cunningham agrees with Deibert and Rohozinski that the information that is presented to the viewers should go through a filter, they disagree with the level of trust that viewers should place in the information that the government makes accessible to them.
News broadcast is fundamentally a method in which information can be most quickly transmitted to a large audience, and regardless the commercial or public roots of the broadcast the basic structure of the news bulletin remains unchanged. However when taking the key differences in ownership, financial funding and intended audience into account, the differences in content and delivery of news are made apparent. To more closely illustrate the specific differences that can be found we will be comparing the public broadcasts of Triple J and ABC News with the commercial broadcasts Gold FM and Seven News where the disparity in news priorities is most evident.
Developing strategies involves hypothesis generation, creating a series of “if-then” statements. This process provides the ability to evaluate program effectiveness, to identify significant objectives and to organize objectives in hierarchical form (Kettner, et. al, 2013). Program design is essentially hypothesis generation. The hypothesis then gets translated into goals and objectives that provide a framework for action, a general direction for the program, a timeframe, details tasks, and clearly states program expectations, which then provides a foundation for monitoring, performance measurement, and evaluation (Kettner, et. al, 2013). The hypothesis questions are translated into a plan, becoming progressively more specific and
This is due to its technical nature and credible sources. Articles are pulled from official reports and data. They follow a format that provides resources, current information, and qualified or expert authorship. Due to the nature of the Database method, there is an inherent ability to be specific about what kind of article is being searched for. Source types, publishers, companies, and other categories can be filtered to provide more relevant information. These sources are also more likely to be Accurate based on their close proximity to reviewed technical
Using this system, analysts can interactively re-rank derived features or select combinations of features, based on which the computation of interesting situations is reorganized and the visualization refreshed. More importantly, visual analytics allows to better include the domain knowledge in the analysis compared to the basic automated approaches.
The process and choice of classifying information is very important. Data of different types have different values to the owner of the information. Some data may be of more value or critical importance than other data. Certain information is therefore valuable, and if lost could cause great financial loss.
The choices for the selection criterion have several model fit statistics that are useful for model
In today’s media-saturated world, traditional media outlets are still considered a very important source for news information, but many people look to outside, specialized media outlets for information that appeals to their personal interests and attitudes. However, there is an increasing concern that journalism is gearing away from factuality, and is instead incorporating opinions and gossip into the mix, as well as breaking news into bits and pieces. Additionally, consolidation among media corporations has affected the industry. Consolidation raises both benefits and concerns: increased diversity, larger conglomerate power, decreased viewpoints, etc. Not only do these rising trends reduce the credibility and diversity of news sources, but they have the potential to flood the media market with low-quality news. Overall, these two trends can be seen in today’s market, and have an enormous effect on the landscape of the journalistic environment.
2. Identify the criteria – This step sometimes requires more than on objective. This step could easily solve other problems. If a criteria is missed, it could change the outcome of the results. However, when all criteria has been identified, it optimizes the results.
Feature selection is a step that finds the subset from the original feature set to some criteria of instance importance. In this paper the concept of group feature selection is review that specifies different approaches in this ground. This literature review examines the recent work of feature selection where feature posses certain group structure. The methods found for group feature selection have discussed here. The general approaches of group feature selection such as group lasso and sparse group lasso are described. The group lasso is an extension of Standard lasso which tend to perform group selection, if the group of features is selected then all the features in the groups are selected. Sparse group lasso takes the advantage of group lasso and produces an efficient solution with simultaneous between and within group. The group feature selection tries to minimize the redundant and irrelevant features from the group to decreases the computational time. The above dimensional categorization of group feature selection algorithm give a view of future challenges and research