Simplifying decision trees

Quinlan, J. R.

doi:10.1006/ijhc.1987.0321

Cited by 316 publications

(290 citation statements)

References 1 publication

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“…In order for an ensemble detector to work, the base detectors have to be diverse; if the detectors are highly correlated, there is little additional value from combining them [26]. In this paper, the diversity is based on different features (general ensemble detector), data sets (mixed ensemble detector), or both (specialized ensemble detector).…”

Section: Related Workmentioning

confidence: 99%

Ensemble Learning for Low-Level Hardware-Supported Malware Detection

Khasawneh

Özsoy

Donovick

et al. 2015

Research in Attacks, Intrusions, and Defenses

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Abstract. Recent work demonstrated hardware-based online malware detection using only low-level features. This detector is envisioned as a first line of defense that prioritizes the application of more expensive and more accurate software detectors. Critical to such a framework is the detection performance of the hardware detector. In this paper, we explore the use of both specialized detectors and ensemble learning techniques to improve performance of the hardware detector. The proposed detectors reduce the false positive rate by more than half compared to a single detector, while increasing the detection rate. We also contribute approximate metrics to quantify the detection overhead, and show that the proposed detectors achieve more than 11x reduction in overhead compared to a software only detector (1.87x compared to prior work), while improving detection time. Finally, we characterize the hardware complexity by extending an open core and synthesizing it on an FPGA platform, showing that the overhead is minimal.

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Section: Related Workmentioning

confidence: 99%

Ensemble Learning for Low-Level Hardware-Supported Malware Detection

Khasawneh

Özsoy

Donovick

et al. 2015

Research in Attacks, Intrusions, and Defenses

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“…Two customers with similar characteristics can easily belong to different classes [31]. Also, credit scoring datasets are typically very noisy [11], particularly the Australian dataset [32]. Figures 2, 3 and 4 display the test set harmonic mean of all 10 classifiers for each of the datasets.…”

Section: Australianmentioning

confidence: 99%

Learning without Default: A Study of One-Class Classification and the Low-Default Portfolio Problem

Kennedy¹,

Namee²,

Delany³

2010

Artificial Intelligence and Cognitive Science

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Abstract. This paper asks at what level of class imbalance one-class classifiers outperform two-class classifiers in credit scoring problems in which class imbalance, referred to as the low-default portfolio problem, is a serious issue. The question is answered by comparing the performance of a variety of one-class and two-class classifiers on a selection of credit scoring datasets as the class imbalance is manipulated. We also include random oversampling as this is one of the most common approaches to addressing class imbalance. This study analyses the suitability and performance of recognised two-class classifiers and one-class classifiers. Based on our study we conclude that the performance of the twoclass classifiers deteriorates proportionally to the level of class imbalance. The two-class classifiers outperform one-class classifiers with class imbalance levels down as far as 15% (i.e. the imbalance ratio of minority class to majority class is 15:85). The one-class classifiers, whose performance remains unvaried throughout, are preferred when the minority class constitutes approximately 2% or less of the data. Between an imbalance of 2% to 15% the results are not as conclusive. These results show that one-class classifiers could potentially be used as a solution to the low-default portfolio problem experienced in the credit scoring domain.

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“…The post-pruning in the FC4.5 implementation uses a so-called reduced error pruning strategy [63], a modification of the post-pruning algorithm presented in Section 4.4, where internal nodes of a fully grown tree are removed one at a time as long as the error is decreasing.…”

Section: Approximationmentioning

confidence: 99%

Decisions: Algebra and Implementation

Danylenko

Lundberg

Löwe

2011

Machine Learning and Data Mining in Pattern Recognition

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Processing decision information is a constitutive part in a number of applications in Computer Science fields. In general, decision information can be used to deduce the relationship between a certain context and a certain decision. Decision information is represented by a decision model that captures this information. Frequently used examples of decision models are decision tables and decision trees. The choice of an appropriate decision model has an impact on application performance in terms of memory consumption and execution time. High memory expenses can possibly occur due to redundancy in a decision model; and high execution time is often a consequence of an unsuitable decision model. Applications in different domains try to overcome these problems by introducing new data structures or algorithms for implementing decision models. These solutions are usually domain-specific and hard to transfer from one domain to another.Different application domains of Computer Science often process decision information in a similar way and, hence, have similar problems. We should thus be able to present a unifying approach that can be applicable in all application domains for capturing and manipulating decision information. Therefore, the goal of this thesis is (i) to suggest a general structure (Decision Algebra) which provides a common theoretical framework that captures decision information and defines operations (signatures) for storing, accessing, merging, approximating, and manipulating such information along with some general algebraic laws regardless of the used implementation. Our Decision Algebra allows defining different construction strategies for decision models and data structures that capture decision information as implementation variants, and it simplifies experimental comparisons between them.Additionally, this thesis presents (ii) an implementation of Decision Algebra capturing the information in a non-redundant way and performing the operations efficiently. In fact, we show that existing decision models that originated in the field of Data Mining and Machine Learning and variants thereof as exploited in special algorithms can be understood as alternative implementation variants of the Decision Algebra by varying the implementations of the Decision Algebra operations. Hence, this work (iii) will contribute to a classification of existing technology for processing decision information in different application domains of Computer Science.

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Simplifying decision trees

Cited by 316 publications

References 1 publication

Ensemble Learning for Low-Level Hardware-Supported Malware Detection

Ensemble Learning for Low-Level Hardware-Supported Malware Detection

Learning without Default: A Study of One-Class Classification and the Low-Default Portfolio Problem

Decisions: Algebra and Implementation

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