Jornada GIC random forests 2012-01-27

objetivo

El objetivo de la jornada es hacer un repaso al estado del arte sobre random forests (y sistemas de clasificación basados en ensambles?). El metodo de trabajo consiste en la presentación por parte de todos los presentes de resumenes de articulos recientes

orden de las presentaciones

• Josu Maiora: tutorial de Criminisi, clasificiacion y regresion slides
• Alexandre Savio: breiman slides_sinrefs
• Iñigo Barandiaran: prehistoria ??
• Maite Termenon: theoretical study Slides
• Ana I. Gonzalez: experimental comparison on ensembles of trees review
• Darya Chyzhyk: incremental ensembles |---Slides
• Ramón Moreno: overfitting cautious Slides
• Elsa Fernandez: seleccion de variables usando RF Slides
• Borja Fernandez-Gauna: learning areas of expertise presentation
• Borja Ayerdi: random feature weights Slides
• Jose Manuel Lopez-Guede: pruning con reinforcement learning presentation
• Borja Ayerdi: statistical pruning Slides
• Israel Rebollo: fuzzy random forests review
• Ion Marques: ensembles from fuzzy clustering presentation
• Miguel A. Veganzones: rotation forests intro slides
• Ana I. Gonzalez: rotation forest empirical review
• Josu Maiora: multilabel y heterogeneos slides
• Eider Sanchez: dynamic ensemble presentacion dynamic
• Darya Chyzhyk: subspace projections |---Slides, |---Slides
• Maite Termenon: varianza residual Slides
• Elsa Fernandez: diversidad Slides, [1]
• Ramón Moreno: aplicaciones Slides
• Jose Manuel Lopez-Guede: data mining presentation
• Alexandre Savio: comparison slides slides_sinrefs
• Eider Sanchez: aplicaciones generales presentacion aplicaciones
• Borja Fernandez-Gauna: building by reward-punishment presentation
• Maite Termenon: streaming random forests Slides
• Miguel A. Veganzones: gene functional inference intro slides
• Ion Marques: action detection presentation
• Alexandre Savio: Ting Agregacion de subespacios de features late slides

distribución de articulos a presentar

• Ana I. Gonzalez
  • experimental comparison on ensembles of trees [2] review
  • rotation forest empirical [3] review

• Josu Maiora
  • tutorial de Criminisi, clasificiacion y regresion [4] slides
  • multilabel y heterogeneos [5] slides

• Borja Ayerdi
  • random feature weights [6]
  • statistical pruning [7] diap

• Iñigo Barandiaran
  • tutorial de Criminisi, density, manyfold, semisupervised [8]
  • prehistoria ?? [9]

• Borja Fernandez-Gauna
  • learning areas of expertise [10] presentation
  • building by reward-punishment [11] presentation

• Jose Manuel Lopez-Guede
  • data mining [12] presentation
  • pruning con reinforcement learning [13] presentation

• Israel Rebollo
  • fuzzy random forests [14] review

• Darya Chyzhyk
  • incremental ensembles [15] |---Slides
  • subspace projections [16] |---Slides, [17] |---Slides

• Elsa Fernandez
  • seleccion de variables usando RF [18] Slides
  • diversidad [19] Slides, [20]

• Maite Termenon
  • varianza residual [21] Slides
  • streaming random forests [22] Slides
  • theoretical study [23] Slides

• Ramón Moreno
  • overfitting cautious [24] Slides
  • aplicaciones [25] Slides

• Miguel A. Veganzones
  • gene functional inference [26] intro slides
  • rotation forests [27] intro slides

• Ion Marques
  • ensembles from fuzzy clustering [28] presentation
  • action detection [29] presentation

• Eider Sanchez
  • dynamic ensemble [30] presentacion dynamic
  • aplicaciones generales [31] presentacion aplicaciones

• Alexandre Savio:
  • Ting Agregacion de subespacios de features [32] -- > no llega late slides
  • comparison [33] slides slides_sinrefs
  • breiman [34] slides slides_sinrefs

referencias

• Groupware general [35]

• Breiman, el padre de la criatura, bagging [36], random forests [37]. Groupware [38]

• tutorial de Criminisi [39], slides [40], pagina personal [41]

• Kuncheva [42], [43], [44], [45], [46], [47], [48], [49], [50]

aplicaciones [51]

• analisis de accidentes [52]
• ingenieria mecanica [53]
• ingenieria financiera [54], [55], [56], [57], [58], [59], [60], [61], [62], [63]

• modelos de lenguage [64], [65], [66],

• biologicas, medicas y ecologicas [67], [68], [69], [70], [71], [72], [73], [74], [75], [76], [77], genomics [78], [79], [80], [81], breast cancer [82], [83], [84], cardiac arrythmia [85], [86], [87], [88], soil texture [89], [90], apendicitis [91], pulmones [92], cancer [93], glaucoma [94], [95], colon cells [96], higado [97], piel [98], de todo ?? [99], celulas [100], ultrasonidos [101], mental fatigue [102], cornea [103]

• quimica [104], [105], [106], [107], [108], [109], [110], [111], [112], [113], [114], [115], [116]

• data mining [117],

• neurociencias [118], [119], [120], fMRI [121], [122], [123], [124], [125], [126], [127], brain extraction [128]

• imagen de reconocimiento remoto, remote sensing, [129], [130], [131], [132], [133], land slides [134], [135], [136], [137], [138], hypespectral data [139], [140], SAR [141]

• pattern recognition [142], [143], biometric data [144], hadwritng [145], signature [146], objetos y acciones [147], [148], [149], visual concept [150], face and action [151]

• intrusion detection [152]