Efficient incremental induction of decision trees

Kalles, Dimitris; Morris, Tim

doi:10.1007/bf00058613

Cited by 46 publications

(37 citation statements)

References 15 publications

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“…The online random forest as proposed by [58] incrementally adopts new features. Updating decision trees with new samples was described by [59,60] and extended by [61,62]. Update schemes for pools of experts like the WINNOW and Weighted Majority Algorithm were introduced by [63,64] and successfully employed since then.…”

Section: Inter-active and Online Learning For Clinical Applicationmentioning

confidence: 99%

Computational pathology: Challenges and promises for tissue analysis

Fuchs

Buhmann

2011

Computerized Medical Imaging and Graphics

231

127

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The histological assessment of human tissue has emerged as the key challenge for detection and treatment of cancer. A plethora of different data sources ranging from tissue microarray data to gene expression, proteomics or metabolomics data provide a detailed overview of the health status of a patient. Medical doctors need to assess these information sources and they rely on data driven automatic analysis tools. Methods for classification, grouping and segmentation of heterogeneous data sources as well as regression of noisy dependencies and estimation of survival probabilities enter the processing workflow of a pathology diagnosis system at various stages. This paper reports on state-of-the-art of the design and effectiveness of computational pathology workflows and it discusses future research directions in this emergent field of medical informatics and diagnostic machine learning.

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Section: Inter-active and Online Learning For Clinical Applicationmentioning

confidence: 99%

Computational pathology: Challenges and promises for tissue analysis

Fuchs

Buhmann

2011

Computerized Medical Imaging and Graphics

231

127

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“…By representing the stereotyping function in this way, we can make use of well-known techniques for inducing decision trees from labeled examples [Frank et al 1998;Kalles and Morris 1996] encapsulate all of an agent's stereotypes about others regarding features in F in one concise structure. Each node of the tree represents a particular feature, and branches from nodes are followed depending on the perceived value of the feature represented by that node.…”

Section: Learning Stereotypesmentioning

confidence: 99%

Stereotypical trust and bias in dynamic multiagent systems

Burnett

Norman

Sycara

2013

ACM Trans. Intell. Syst. Technol.

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Large-scale multiagent systems have the potential to be highly dynamic. Trust and reputation are crucial concepts in these environments, as it may be necessary for agents to rely on their peers to perform as expected, and learn to avoid untrustworthy partners. However, aspects of highly dynamic systems introduce issues which make the formation of trust relationships difficult. For example, they may be short-lived, precluding agents from gaining the necessary experiences to make an accurate trust evaluation. This article describes a new approach, inspired by theories of human organizational behavior, whereby agents generalize their experiences with previously encountered partners as stereotypes, based on the observable features of those partners and their behaviors. Subsequently, these stereotypes are applied when evaluating new and unknown partners. Furthermore, these stereotypical opinions can be communicated within the society, resulting in the notion of stereotypical reputation. We show how this approach can complement existing state-of-the-art trust models, and enhance the confidence in the evaluations that can be made about trustees when direct and reputational information is lacking or limited. Furthermore, we show how a stereotyping approach can help agents detect unwanted biases in the reputational opinions they receive from others in the society.

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“…Decision trees. The problem of processing streaming data in online has motivated the development of many algorithms which were designed to learn decision trees incrementally [16], [17]. Some examples of the algorithms which construct incremental decision trees are: ID4 [18], ID5 [19], and ID5R [20].…”

Section: Background and Related Workmentioning

confidence: 99%

Creating Evolving User Behavior Profiles Automatically

Iglesias

Angelov

Ledezma

et al. 2012

IEEE Trans. Knowl. Data Eng.

115

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Knowledge about computer users is very beneficial for assisting them, predicting their future actions or detecting masqueraders. In this paper, a new approach for creating and recognizing automatically the behavior profile of a computer user is presented. In this case, a computer user behavior is represented as the sequence of the commands she/he types during her/his work. This sequence is transformed into a distribution of relevant subsequences of commands in order to find out a profile that defines its behavior. Also, because a user profile is not necessarily fixed but rather it evolves/changes, we propose an evolving method to keep up to date the created profiles using an Evolving Systems approach. In this paper, we combine the evolving classifier with a trie-based user profiling to obtain a powerful self-learning online scheme. We also develop further the recursive formula of the potential of a data point to become a cluster center using cosine distance, which is provided in the Appendix. The novel approach proposed in this paper can be applicable to any problem of dynamic/evolving user behavior modeling where it can be represented as a sequence of actions or events. It has been evaluated on several real data streams.

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Efficient incremental induction of decision trees

Cited by 46 publications

References 15 publications

Computational pathology: Challenges and promises for tissue analysis

Computational pathology: Challenges and promises for tissue analysis

Stereotypical trust and bias in dynamic multiagent systems

Creating Evolving User Behavior Profiles Automatically

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