InstanceRank: Bringing order to datasets

In this paper we present InstanceRank, a ranking algorithm that reflects the relevance of the instances within a dataset. InstanceRank applies a similar solution to that used by PageRank, the web pages ranking algorithm in the Google search engine. We also present ISR, an instance selection technique that uses InstanceRank. This algorithm chooses the most representative instances from a learning database. Experiments show that ISR algorithm, with InstanceRank as ranking criteria, obtains similar results in accuracy to other instance reduction techniques, noticeably reducing the size of the instance set.Ministerio de Educación y Ciencia HUM2007-66607-C04-0

PolarityRank: Finding an equilibrium between followers and contraries in a network

In this paper we present the relevance ranking algorithm named PolarityRank. This algorithm is inspired in PageRank, the webpage relevance calculus method used by Google, and generalizes it to deal with graphs having not only positive but also negative weighted arcs. Besides the definition of our algorithm, this paper includes the algebraic justification, the convergence demonstration and an empirical study in which PolarityRank is applied to two unrelated tasks where a graph with positive and negative weights can be built: the calculation of word semantic orientation and instance selection from a learning dataset

WIRS. Un Algoritmo de Reducción de Instancias Basado en Ranking

En este artículo se presenta el algoritmo WIRS, una técnica de reducción de instancias que tiene como objetivo seleccionar las ins tancias más representativas de una base de datos de aprendizaje. Este tipo de técnicas se utilizan para conseguir bases de datos más pequeñas sobre las que se pueda aplicar el algoritmo de los vecinos más cercanos con menor coste computacional y sin excesiva pérdida de precisión. El algoritmo WIRS es una adaptación del algoritmo WITS en el que se ha sustituido el criterio de la tipicidad por el de ranking a la hora de calcular el orden de las instancias necesario para aplicar WITS. Para calcular el ranking utilizamos una solución similar a la empleada por PageRank, el algoritmo de cálculo de relevancia de páginas web del buscador Google. Los experimentos demuestran que el uso del ranking como criterio de ordenación obtiene resultados comparables a los obtenidos por la versión original de WITS, mejorando incluso estos resultados para algunas de las bases de datos utilizadas.Ministerio de Educación y Ciencia TIN 2004-07246-C03-0

Weighted Instance Typicality Search (WITS): A nearest neighbor data reduction algorithm

Two disadvantages of the standard nearest neighbor algorithm are 1) it must store all the instances of the training set, thus creating a large memory footprint and 2) it must search all the instances of the training set to predict the classification of a new query point, thus it is slow at run time. Much work has been done to remedy these shortcomings. This paper presents a new algorithm WITS (Weighted-Instance Typicality Search) and a modified version, Clustered-WITS (C-WITS), designed to address these issues. Data reduction algorithms address both issues by storing and using only a portion of the available instances. WITS is an incremental data reduction algorithm with O(n 2) complexity, where n is the training set size. WITS uses the concept of Typicality in conjunction with Instance-Weighting to produce minimal nearest neighbor solutions. WITS and C-WITS are compared to three other state of the art data reduction algorithms on ten real-world datasets. WITS achieved the highest average accuracy, showed fewer catastrophic failures, and stored an average of 71 % fewer instances than DROP-5, the next most competitive algorithm in terms of accuracy and catastrophic failures. The C-WITS algorithm provides a user-defined parameter that gives the user control over the training-time vs. accuracy balance. This modification makes C-WITS more suitable for large problems, the very problems data reductions algorithms are designed for. On two large problems (10,992 and 20,000 instances), C-WITS stores only a small fraction of the instances (0.88 % and 1.95 % of the training data) while maintaining generalization accuracies comparable to the best accuracies reported for these problems

Review of Particle Physics

The Review summarizes much of particle physics and cosmology. Using data from previous editions, plus 2,143 new measurements from 709 papers, we list, evaluate, and average measured properties of gauge bosons and the recently discovered Higgs boson, leptons, quarks, mesons, and baryons. We summarize searches for hypothetical particles such as supersymmetric particles, heavy bosons, axions, dark photons, etc. Particle properties and search limits are listed in Summary Tables. We give numerous tables, figures, formulae, and reviews of topics such as Higgs Boson Physics, Supersymmetry, Grand Unified Theories, Neutrino Mixing, Dark Energy, Dark Matter, Cosmology, Particle Detectors, Colliders, Probability and Statistics. Among the 120 reviews are many that are new or heavily revised, including a new review on Machine Learning, and one on Spectroscopy of Light Meson Resonances. The Review is divided into two volumes. Volume 1 includes the Summary Tables and 97 review articles. Volume 2 consists of the Particle Listings and contains also 23 reviews that address specific aspects of the data presented in the Listings

Review of Particle Physics

The Review summarizes much of particle physics and cosmology. Using data from previous editions, plus 2,143 new measurements from 709 papers, we list, evaluate, and average measured properties of gauge bosons and the recently discovered Higgs boson, leptons, quarks, mesons, and baryons. We summarize searches for hypothetical particles such as supersymmetric particles, heavy bosons, axions, dark photons, etc. Particle properties and search limits are listed in Summary Tables. We give numerous tables, figures, formulae, and reviews of topics such as Higgs Boson Physics, Supersymmetry, Grand Unified Theories, Neutrino Mixing, Dark Energy, Dark Matter, Cosmology, Particle Detectors, Colliders, Probability and Statistics. Among the 120 reviews are many that are new or heavily revised, including a new review on Machine Learning, and one on Spectroscopy of Light Meson Resonances. The Review is divided into two volumes. Volume 1 includes the Summary Tables and 97 review articles. Volume 2 consists of the Particle Listings and contains also 23 reviews that address specific aspects of the data presented in the Listings. The complete Review (both volumes) is published online on the website of the Particle Data Group (pdg.lbl.gov) and in a journal. Volume 1 is available in print as the PDG Book. A Particle Physics Booklet with the Summary Tables and essential tables, figures, and equations from selected review articles is available in print, as a web version optimized for use on phones, and as an Android app

Review of Particle Physics

The Review summarizes much of particle physics and cosmology. Using data from previous editions, plus 2,143 new measurements from 709 papers, we list, evaluate, and average measured properties of gauge bosons and the recently discovered Higgs boson, leptons, quarks, mesons, and baryons. We summarize searches for hypothetical particles such as supersymmetric particles, heavy bosons, axions, dark photons, etc. Particle properties and search limits are listed in Summary Tables. We give numerous tables, figures, formulae, and reviews of topics such as Higgs Boson Physics, Supersymmetry, Grand Unified Theories, Neutrino Mixing, Dark Energy, Dark Matter, Cosmology, Particle Detectors, Colliders, Probability and Statistics. Among the 120 reviews are many that are new or heavily revised, including a new review on Machine Learning, and one on Spectroscopy of Light Meson Resonances. The Review is divided into two volumes. Volume 1 includes the Summary Tables and 97 review articles. Volume 2 consists of the Particle Listings and contains also 23 reviews that address specific aspects of the data presented in the Listings. The complete Review (both volumes) is published online on the website of the Particle Data Group (pdg.lbl.gov) and in a journal. Volume 1 is available in print as the PDG Book. A Particle Physics Booklet with the Summary Tables and essential tables, figures, and equations from selected review articles is available in print, as a web version optimized for use on phones, and as an Android app.United States Department of Energy (DOE) DE-AC02-05CH11231government of Japan (Ministry of Education, Culture, Sports, Science and Technology)Istituto Nazionale di Fisica Nucleare (INFN)Physical Society of Japan (JPS)European Laboratory for Particle Physics (CERN)United States Department of Energy (DOE