Local Boosting of Decision Stumps for Regression and Classification Problems

Kotsiantis, Sotiris; Kanellopoulos, Dimitris; Pintelas, Panayiotis E.

doi:10.4304/jcp.1.4.30-37

Cited by 79 publications

(70 citation statements)

References 18 publications

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“…This classifier is available in the MOA framework 4 [17], supporting massive data streams and several modeling scenarios (statistics, clustering, and classification) from these. f) Decision stump [52]: one-level decision trees that are updatable by appropriate counts in the information criterion; in addition, this classifier is available in the MOA tool. g) IB1 [45], [53]: an instanced-based classifier [54] where the reference update base is growing-for each classification query, the whole history is available instead of single snapshots of the data stream.…”

Section: Discussionmentioning

confidence: 99%

Reliable All-Pairs Evolving Fuzzy Classifiers

Lughofer

Buchtala

2013

IEEE Trans. Fuzzy Syst.

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In this paper, we propose a novel design of evolving fuzzy classifiers (EFCs) to handle online multiclass classification problems in a data-streaming context. Therefore, we exploit the concept of all-pairs (AP), a.k.a. all-versus-all, classification using binary classifiers for each pair of classes. This benefits from less complex decision boundaries to be learned, as opposed to a direct multiclass approach, and achieves a higher efficiency in terms of incremental training time than one-versus-rest classification techniques. For the binary classifiers, we apply fuzzy classifiers with singleton class labels in the consequences, as well as Takagi-Sugeno (T-S) fuzzy models to conduct regression on [0, 1] for each class pair. Both are evolved and incrementally trained in a data-streaming context, yielding a permanent update of the whole AP collection of classifiers, thus being able to properly react to dynamic changes in the streams. The classification phase considers a novel strategy by using the preference levels of each pair of classes that are collected in a preference relation matrix and performing a weighted voting scheme on this matrix. This is done by investigating the reliability of the classifiers in their predictions: 1) integrating the degree of ignorance on samples to be classified as weights for the preference levels and 2) new conflict models used in the single binary classifiers and when calculating the final class response based on the preference relation matrix. The advantage of the new EFC concept over the single model (using a direct multiclass classification concept) and multimodel architectures (using a one-versus-rest classification concept) will be underlined by empirical evaluations and comparisons at the end of this paper based on high-dimensional real-world multiclass classification problems. The results also show that integrating conflict and ignorance concepts into the preference relations can boost classifier accuracies.

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Section: Discussionmentioning

confidence: 99%

Reliable All-Pairs Evolving Fuzzy Classifiers

Lughofer

Buchtala

2013

IEEE Trans. Fuzzy Syst.

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“…Discretization means the partition of a given interval into a set of discrete sub-intervals, and split continuous intervals into two or more sub-intervals which can be treated as nominal values. A good discretization method should well consider the balance between the information loss and partition numbers [36]. Depending on whether or not they involve target variable information, discretization methods are typically organized in two groups, i.e., the supervised and unsupervised discretization, respectively.…”

Section: Applications In General Sciencesmentioning

confidence: 99%

Shannon information entropy in heavy-ion collisions

2018

Progress in Particle and Nuclear Physics

104

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The general idea of information entropy provided by C.E. Shannon "hangs over everything we do" and can be applied to a great variety of problems once the connection between a distribution and the quantities of interest is found. The Shannon information entropy essentially quantify the information of a quantity with its specific distribution, for which the information entropy based methods have been deeply developed in many scientific areas including physics. The dynamical properties of heavy-ion collisions (HICs) process make it difficult and complex to study the nuclear matter and its evolution, for which Shannon information entropy theory can provide new methods and observables to understand the physical phenomena both theoretically and experimentally. To better understand the processes of HICs, the main characteristics of typical models, including the quantum molecular dynamics models, thermodynamics models, and statistical models, etc, are briefly introduced. The typical applications of Shannon information theory in HICs are collected, which cover the chaotic behavior in branching process of hadron collisions, the liquid-gas phase transition in HICs, and the isobaric difference scaling phenomenon for intermediate mass fragments produced in HICs of neutron-rich systems. Even though the present applications in heavy-ion collision physics are still relatively simple, it would shed light on key questions we are seeking for. It is suggested to further develop the information entropy methods in nuclear reactions models, as well as to develop new analysis methods to study the properties of nuclear matters in HICs, especially the evolution of dynamics system.

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“…Thus, the k 1 was set to 5 and k 2 =50. since at about this size of instances, it is appropriate for a simple algorithm to build a precise model [14]. The proposed method is presented in Algorithm 1.…”

Section: The Proposed Algorithmmentioning

confidence: 99%