10,206 research outputs found
An information assistant system for the prevention of tunnel vision in crisis management
In the crisis management environment, tunnel vision is a set of bias in decision makersā cognitive process which often leads to incorrect understanding of the real crisis situation, biased perception of information, and improper decisions. The tunnel vision phenomenon is a consequence of both the challenges in the task and the natural limitation in a human beingās cognitive process. An information assistant system is proposed with the purpose of preventing tunnel vision. The system serves as a platform for monitoring the on-going crisis event. All information goes through the system before arrives at the user. The system enhances the data quality, reduces the data quantity and presents the crisis information in a manner that prevents or repairs the userās cognitive overload. While working with such a system, the users (crisis managers) are expected to be more likely to stay aware of the actual situation, stay open minded to possibilities, and make proper decisions
A Decision tree-based attribute weighting filter for naive Bayes
The naive Bayes classifier continues to be a popular learning algorithm for data mining applications due to its simplicity and linear run-time. Many enhancements to the basic algorithm have been proposed to help mitigate its primary weakness--the assumption that attributes are independent given the class. All of them improve the performance of naĆÆve Bayes at the expense (to a greater or lesser degree) of execution time and/or simplicity of the final model. In this paper we present a simple filter method for setting attribute weights for use with naive Bayes. Experimental results show that naive Bayes with attribute weights rarely degrades the quality of the model compared to standard naive Bayes and, in many cases, improves it dramatically. The main advantages of this method compared to other approaches for improving naive Bayes is its
run-time complexity and the fact that it maintains the simplicity of the final model
On the representational bias in process mining
Process mining serves a bridge between data mining and business process modeling. The goal is to extract process related knowledge from event data stored in information systems. One of the most challenging process mining tasks is process discovery, i.e., the automatic construction of process models from raw event logs. Today there are dozens of process discovery techniques generating process models using different notations (Petri nets, EPCs, BPMN, heuristic nets, etc.). This paper focuses on the representational bias used by these techniques. We will show that the choice of target model is very important for the discovery process itself. The representational bias should not be driven by the desired graphical representation but by the characteristics of the underlying processes and process discovery techniques. Therefore, we analyze the role of the representational bias in process mining
A Framework for Understanding Unintended Consequences of Machine Learning
As machine learning increasingly affects people and society, it is important
that we strive for a comprehensive and unified understanding of potential
sources of unwanted consequences. For instance, downstream harms to particular
groups are often blamed on "biased data," but this concept encompass too many
issues to be useful in developing solutions. In this paper, we provide a
framework that partitions sources of downstream harm in machine learning into
six distinct categories spanning the data generation and machine learning
pipeline. We describe how these issues arise, how they are relevant to
particular applications, and how they motivate different solutions. In doing
so, we aim to facilitate the development of solutions that stem from an
understanding of application-specific populations and data generation
processes, rather than relying on general statements about what may or may not
be "fair."Comment: 6 pages, 2 figures; updated with corrected figure
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