dataesr/scientific-paragraphs-categorization huggingface dataset:
abhishek4 AutoCompete of  (Thakur and Krohn-Grimberghe, 2015)  is an automated machine learning framework for tackling Machine Learning competitions. This solution performed well in late rounds of the AutoML challenge and won the GPU track  (Thakur, 2016). The pipeline includes (1) stratified data splitting, (2) building features, (3) feature selection, (4) performing model and hyper-parameter selection (Random Forests, Logistic Regression, Ridge Regression, Lasso, SVM, Naive Bayes, and Nearest Neighbors), and (5) ensembling solutions. Search space is specified with prior knowledge on similar data sets (a form of meta-learning). Thakur found that this strategy is faster and yields comparable results to hyperopt. The underlying implementation is based purely on Python and scikit-learn with some modules in Cython.
Their GPU solution is an advanced version of the AutoCompete solution, which uses Neural Networks built with Keras  (Chollet, 2015). djajetic Djajetic  (Jajetic, 2016a)  is based on heterogeneous ensembles of models obtained by searching through model-space and adjusting hyper-parameters (HP) without any communication between models. Jajetic believes that this makes search more effective in non-convex search spaces. This strategy lends itself well to efficient and simple parallelization. The search space and ensembling properties for each individual model is defined in a separate Python script. Each model is trained and explores its own parameter space and only communicates its training error and best prediction results to the outside. The ensembling module operates in a hierarchical manner.
aad freiburg PoSH Auto-sklearn (Portfolio Successive Halving combined with Auto-sklearn) is the solution of the aad freiburg team, which obtained the best performance in the 2018 challenge. PoSH Auto-sklearn uses a fixed portfolio of machine learning pipeline configurations on which it performs successive halving. If there is time left, it uses the outcome of these runs to warmstart a combination of Bayesian optimization and successive halving. Greedy submodular function maximization was used on a large performance matrix of ≈421 configurations run on ≈421 datasets to obtain a portfolio of configurations that performs well on a diverse set of datasets. To obtain the matrix, aad freiburg used SMAC  (Hutter et al. )  to search the space of configurations offline, separately for each of the ≈421 datasets.
The configuration space was a subspace of the Auto-sklearn configuration space: dataset preprocessing (feature scaling, imputation of missing value, treatment of categorical values), but no feature preprocessing (this constraint due to the short time limits / resources in the competition), and one of SVM, Random Forest, Linear Classification (via SGD) or XG-Boost. The combination of Bayesian optimization and successive halving is an adaptation of a newly developed method dubbed BO-HB (Bayesian Optimization HyperBand)  (Falkner et al. , 2018). The solution was further designed to yield robust results within the short time limits as follows: the number of iterations was used as a budget, except for the SVM, where the dataset size was the budget. If the dataset had less than 1000 data points, they reverted to simple cross-validation instead of successive halving.
narnars0 The narnars0 team proposed an Automated Machine Learning System for Voting Classifier with Various Tree-Based Classifiers. This team based their solution in a voting ensemble formed with the following tree-based classifiers: gradient boosting, random forests, and extra-trees classifiers. They optimized the hyperparameters of tree-based classifiers by means of Bayesian optimization. Several machine learning models in scikit-learn were used to implement this system, including narnars0's own Bayesian optimization package, bayeso (https://github. com/jungtaekkim/bayeso), which was used to optimize a selection of hyperparameters of classifiers. wlWangl An AutoML solution resembling Q-Learning in reinforcement learning was proposed by the wlWangl team. This team considers the machine learning design pipeline as composed of three phases: data preprocessing, feature selection, and classification.
thanhdng The solution bt thanhdng was based on the ensemble solution provided as starting kit for the competition. Basically, this team adjusted the parameters of the ensemble (increasing the number of learning cycles and estimators). We also show in Figures 17-23 all learning curves of a given round ovelaid for the same two high ranking participants ('aad freiburg' (solid-dots) and 'abhishek' (solid-empty square)). This representation shows that the two optimization strategies differ in their management of time. The 'aad freiburg' made use of parallelism. Since 4 cores were available on the computers used for the challenge, they started working on 4 (out of 5) datasets simultaneously and started on the fifth one by interrupting working on one of the other datasets, or interleaving work. In contrast, 'abhishek' processed one dataset after the other.
LIF, UMR CNRS 7279) of the University of Aix Marseille, France, via the LabeX Archimede program, the Laboratoire de Recheche en Informatique of Paris Sud University, and INRIA-Saclay as part of the TIMCO project, as well as the support from the Paris-Saclay Center for Data Science (CDS). Additional computer resources were provided generously by J. Buhmann, ETH Zürich. This work has been partially supported by the Spanish project TIN2016-74946-P (MINECO/FEDER, UE) and CERCA Programme / Generalitat de Catalunya. The datasets released were selected among 72 datasets that were donated (or formatted using data publicly available) by the co-authors and by: Y. Aphinyanaphongs, O. Chapelle, Z. Iftikhar Malhi, V. Lemaire, C. -J. Lin, M. Madani, G. Stolovitzky, H. -J. Thiesen, and I. Tsamardinos. Many people provided feedback to early designs of the protocol and/or tested the challenge platform, including: K.
Bennett, C. Capponi, G. Cawley, R. Caruana, G. Dror, T. K. Ho, B. Kégl, H. Larochelle, V. Lemaire, C. -J. Lin, V. Ponce López, N. Macia, S. Mercer, F. Popescu, D. Silver, S. Treguer, and I. Tsamardinos. The software developers who contributed to the implementation of the Codalab platform and the sample code include E. Camichael, I. Chaabane, I. Judson, C. Poulain, P. Liang, A. Pesah, L. Romaszko, X. Baro Solé, E. Watson, F. Zhingri, M. Zyskowski. Some initial analyses of the challenge results were performed by I. Chaabane, J. Lloyd, N. Macia, and A. Thakur were incorporated in this paper. Katharina Eggensperger, Syed Mohsin Ali and Matthias Feurer helped with the organization of the Beat AutoSKLearn challenge. Matthias Feurer also contributed to the simulations of running auto-sklearn on 2015-2016 challenge datasets.
ClassProbabilityMin = min i=1. n (p(Class i )) = min i=1. n ( N umberOf Instances Class i T otleN umberOf Instances ) • ClassProbabilityMax = max i=1. n (p(Class i )) = max i=1. n ( N umberOf Instances Class i T otleN umberOf Instances ) • ClassEntropy = mean(-n i=1 p(Class i )ln(p(Class i ))) where p(Class i ) is the probability of having an instance of Class i • ClassOccurences = number of examples for each class • ClassProbabilityMean = mean( ClassOcurrences N umberOf Classes ) LogInverseDatasetRatio = log(DatasetRatio) 15. Kurtosis, Skewness, KurtosisPCA and SkewnessPCA are intermediate metafeatures used to calculate some other metafeatures.
Datasets of the 2015/2016 AutoML challenge. C=number of classes. Cbal=class balance. Sparse=sparsity. Miss=fraction of missing values. Cat=categorical variables. Irr=fraction of irrelevant variables. Pte, Pva, Ptr=number of examples of the test, validation, and training sets, respectively. N=number of features. Ptr/N=aspect ratio of the dataset.
area included varies inversely with the population density. They computed distances among the centroids of each block group as measured in latitude and longitude. The final data contained 20,640 observations on 9 variables. The dependent variable is ln(median house value). For the purpose of the AutoML challenge, all samples were merged and the data were freshly randomly split in three sets: training, validation, and test. The order of the features was randomized, after adding a few distractor features (probes) that are permuted versions of real features. This dataset was prepared by Isabelle Guyon from original data provided by Yann LeCun, Corinna Cortes, and Chris Burges: "MNIST handwritten digit dataset" (http: //yann. lecun. com/exdb/mnist/). Past Usage: Many methods have been tried on the MNIST database, in its original data split (60,000 training examples, 10,000 test examples, 10 classes).
DOROTHEA was prepared for the NIPS 2003 variable and feature selection benchmark by Isabelle Guyon, 955 Creston Road, Berkeley, CA 94708, USA (isabelle@clopinet. com). The dataset with which DOROTHEA was created is one of the KDD (Knowledge Discovery in Data Mining) Cup 2001. The original dataset and papers of the winners of the competition are available at: http://www. cs. wisc. edu/ ~dpage/ kddcup2001/. DuPont Pharmaceuticals graciously provided this data set for the KDD Cup 2001 competition. All publications referring to analysis of this data set should acknowledge DuPont Pharmaceuticals Research Laboratories and KDD Cup 2001.
This dataset was prepared by Isabelle Guyon from original data provided by Reza Farrahi Moghaddam, Mathias Adankon, Kostyantyn Filonenko, Robert Wisnovsky, and Mohamed Cheriet: "Arabic manuscripts" (http://www. causality. inf. ethz. ch/activelearning. php?page=datasets). Past Usage: Active learning challenge, A dataset, see http://www. causality. inf. ethz. ch/activelearning. php. Description: The task is to classify cursive script subwords from data in a feature representation extracted from Arabic Historical Manuscripts. Preparation: We modified the original data split and added probes.
This dataset was prepared by Isabelle Guyon from original data provided by Emmanuel Faure, Thierry Savy, Louise Duloquin, Miguel Luengo Oroz, Benoit Lombardot, Camilo Melani, Paul Bourgine, and Nadine Peyrieras: "Mitosis classification" (http:// www. causality. inf. ethz. ch/activelearning. php?page=datasets). Past Usage: Active learning challenge, E dataset, see http://www. causality. inf. ethz. ch/activelearning. php. Description: A feature representation of cells of zebrafish embryo to determine whether they are in division (meiosis) or not. All the examples are manually annotated. Preparation: We modified the original data split and added probes. Representation: Features extracted from video data. This dataset was prepared by Isabelle Guyon from original data provided by Department of Forest Sciences, Colorado: "Forest cover types" (https://archive.
This dataset was prepared by Bisakha Ray from original data provided by Bisakha Ray, Javier Orlandi, Olav Stetter, Isabelle Guyon: "Connectomics-features-normal-1" (http: //www. kaggle. com/c/connectomics/leaderboard). Past Usage: Used for Connectomics challenge at http://www. kaggle. com/c/connectomics/leaderboard. Description: This is a dataset of Connectomics Challenge. The outcome considered is presence or absence of connection.
This dataset is a version of the Adult data set used in round 0 of the 2015/2016 AutoML challenge. It was prepared by Isabelle Guyon from original data extracted by Barry Becker from the 1994 Census database. The data was donated to the UCI repository by Ron Kohavi: "Adult data set" (https://archive. ics. uci. edu/ml/datasets/Adult). Past Usage: It was used previously used in the Performance Prediction challenge, the Model Selection game, and the Agnostic Learning vs. Prior Knowledge (ALvsPK) challenge. Adult, a version of ADA was used in round 0 of the 2015/2016 AutoML challenge.
This dataset was made available by Isabelle Guyon. The tasks consist in distinguishing cancer versus normal patterns from mass-spectrometric data. This is a two-class classification problem with continuous input variables. More information on the dataset is available from this link: https://archive. ics. uci. edu/ml/datasets/ArcenePastUsage: The Arcene dataset has been used previously in the NIPS 2003 feature selection challenge. Description:The data were obtained from two sources: The National Cancer Institute (NCI) and the Eastern Virginia Medical School (EVMS). All the data consist of mass-spectra obtained with the SELDI technique. The samples include patients with cancer (ovarian or prostate cancer), and healthy or control patients. Ovarian cancer samples comprise 253 spectra, including 91 controls and 162 cancer spectra.
( LIF, UMR CNRS 7279) of the University of Aix Marseille, France, via the LabeX Archimede program, the Laboratoire de Recheche en Informatique of Paris Sud University, and INRIA-Saclay as part of the TIMCO project, as well as the support from the Paris-Saclay Center for Data Science (CDS). Additional computer resources were provided generously by J. Buhmann, ETH Zürich. This work has been partially supported by the Spanish project TIN2016-74946-P (MINECO/FEDER, UE) and CERCA Programme / Generalitat de Catalunya. The datasets released were selected among 72 datasets that were donated (or formatted using data publicly available) by the co-authors and by: Y. Aphinyanaphongs, O. Chapelle, Z. Iftikhar Malhi, V. Lemaire, C. -J. Lin, M. Madani, G. Stolovitzky, H. -J. Thiesen, and I. Tsamardinos. Many people provided feedback to early designs of the protocol and/or tested the challenge platform, including: K.
15 :•• 15 ClassProbabilityMin = min i=1. n (p(Class i )) = min i=1. n ( N umberOf Instances Class i T otleN umberOf Instances ) • ClassProbabilityMax = max i=1. n (p(Class i )) = max i=1. n ( N umberOf Instances Class i T otleN umberOf Instances ) • ClassEntropy = mean(-n i=1 p(Class i )ln(p(Class i ))) where p(Class i ) is the probability of having an instance of Class i • ClassOccurences = number of examples for each class • ClassProbabilityMean = mean( ClassOcurrences N umberOf Classes ) LogInverseDatasetRatio = log(DatasetRatio) 15. Kurtosis, Skewness, KurtosisPCA and SkewnessPCA are intermediate metafeatures used to calculate some other metafeatures.
This dataset was used in the NIPS 2003 Feature Selection Challenge under the name GISETTE and in the WCCI 2006 Performance Prediction Challenge and the IJCNN 2007 Agnostic Learning vs. Prior Knowledge Challenge under the name GINA, and in the ICML 2011 Unsupervised and Transfer Learning Challenge under the name ULE. Representation: Features. SET 0. 3: DIGITS Task Metric C Cbal Sparse Miss Cat Irr Pte Pva Ptr N Ptr/N multiclass BAC 10 1 0. 42 0 0 0. 5 35000 20000 15000 1568 9. 57 • NumberOfInstancesWithMissingValues: Number of examples containing missing values. geographical.
DOROTHEA was prepared for the NIPS 2003 variable and feature selection benchmark by Isabelle Guyon, 955 Creston Road, Berkeley, CA 94708, USA (isabelle@clopinet. com). The dataset with which DOROTHEA was created is one of the KDD (Knowledge Discovery in Data Mining) Cup 2001. The original dataset and papers of the winners of the competition are available at: http://www. cs. wisc. edu/ ~dpage/ kddcup2001/. DuPont Pharmaceuticals graciously provided this data set for the KDD Cup 2001 competition. All publications referring to analysis of this data set should acknowledge DuPont Pharmaceuticals Research Laboratories and KDD Cup 2001. Task Metric C Cbal Sparse Miss Cat Irr Pte Pva Ptr N Ptr/N binary AUC 2 0. 46 0. 99 0 0 0. 5 800 350 800 100000 0. 01 This dataset was prepared by Isabelle Guyon from original data provided by DuPont Pharmaceuticals: "Feature selection challenge data" (http://www.
This dataset was prepared by Isabelle Guyon from original data provided by Reza Farrahi Moghaddam, Mathias Adankon, Kostyantyn Filonenko, Robert Wisnovsky, and Mohamed Cheriet: "Arabic manuscripts" (http://www. causality. inf. ethz. ch/activelearning. php?page=datasets). Past Usage: Active learning challenge, A dataset, see http://www. causality. inf. ethz. ch/activelearning. php. Description: The task is to classify cursive script subwords from data in a feature representation extracted from Arabic Historical Manuscripts. Preparation: We modified the original data split and added probes. Task Metric C Cbal Sparse Miss Cat Irr Pte Pva Ptr N Ptr/N binary BAC 2 1 0. 78 0 0 0. 5 1756 526 2984 144 20. 72 Representation: Features/Attributes. SET 1. 3: MADELINE Task Metric C Cbal Sparse Miss Cat Irr Pte Pva Ptr N Ptr/N binary BAC 2 1 1.
ics. uci. edu/ml/datasets/Covertype). Past Usage: Active learning challenge, F dataset, see http://www. causality. inf. ethz. ch/activelearning. php. Description: The tasks is to classify forest cover types. The original multiclass problem is brought back to Krummholz vs. other classes of trees. Preparation: We modified the original data split and added probes. Task Metric C Cbal Sparse Miss Cat Irr Pte Pva Ptr N Ptr/N binary BAC 2 1 0. 0012 0 0 0. 5 4664 1166 5832 308 18. 94 SET 1. 5: SYLVINE Task Metric C Cbal Sparse Miss Cat Irr Pte Pva Ptr N Ptr/N binary BAC 2 1 0. 01 0 0 0. 5 10244 5124 5124 20 256. 2 Representation: Features/Attributes ROUND 2 SET 2. 1: ALBERT Task Metric C Cbal Sparse Miss Cat Irr Pte Pva Ptr N Ptr/N binary F1 2 1 0. 049 0. 14 1 0. 5 51048 25526 425240 78 5451. 79.
This dataset was prepared by Mehreen Saeed from original data provided by Sarmad Hussain and Qurat ul Ain Akram: "Urdu OCR dataset" (http://www. cle. org. pk/ clestore/imagecorpora. htm). Task Metric C Cbal Sparse Miss Cat Irr Pte Pva Ptr N Ptr/N multiclass BAC 355 1 0. 11 0 0 0 12000 6000 416188 60 6936. 47 Past Usage: http://www. cle. org. pk/Publication/papers/2013/Binarization%20and% 20its%20Evaluation%20for%20Urdu%20Nastalique%20Document%20Images%208-3-1. pdf, http://www. cle. org. pk/Publication/papers/2014/AdaptingTesseract%20for%20Complex% 20Scripts-%20an%20Example%20for%20Nastalique%203. 10. pdf,www. UrduOCR. netandwww. cle. org. pk/clestore/imagecorpora. htm.
This dataset was prepared by Bisakha Ray from original data provided by Bisakha Ray, Javier Orlandi, Olav Stetter, Isabelle Guyon: "Connectomics-features-normal-1" (http: //www. kaggle. com/c/connectomics/leaderboard). Past Usage: Used for Connectomics challenge at http://www. kaggle. com/c/connectomics/leaderboard. Description: This is a dataset of Connectomics Challenge. The outcome considered is presence or absence of connection. Task Metric C Cbal Sparse Miss Cat Irr Pte Pva Ptr N Ptr/N binary PAC 2 0. 097 0. 0027 0 0 0. 5 10000 10000 50000 1070 46. 73.
This dataset is a version of the Adult data set used in round 0 of the 2015/2016 AutoML challenge. It was prepared by Isabelle Guyon from original data extracted by Barry Becker from the 1994 Census database. The data was donated to the UCI repository by Ron Kohavi: "Adult data set" (https://archive. ics. uci. edu/ml/datasets/Adult). Past Usage: It was used previously used in the Performance Prediction challenge, the Model Selection game, and the Agnostic Learning vs. Prior Knowledge (ALvsPK) challenge. Adult, a version of ADA was used in round 0 of the 2015/2016 AutoML challenge. C. 1. PHASE 1: development SET 1. 1: ADA Task Metric C Cbal Sparse Miss Cat Irr Pte Pva Ptr N Ptr/N binary AUC 2 1 0. 33 0 0 0 41471 415 4147 48 86. 39.
Regarding the prostate cancer, there are 253 normal samples and 69 disease samples. The number of original features is 15154. Preparation: The samples were prepared as described in http://clopinet. com/ isabelle/Projects/NIPS2003/Slides/NIPS2003-Datasets. pdf. After preprocessing, 3000 informative features and 7000 probes were included in the data set. Representation: See http://clopinet. com/isabelle/Projects/NIPS2003/Slides/ NIPS2003-Datasets. pdf. SET 1. 3: GINA Task Metric C Cbal Sparse Miss Cat Irr Pte Pva Ptr N Ptr/N binary AUC 2 1 0. 97 0. 31 0 0 31532 315 3153 970 3. 25 Task Metric C Cbal Sparse Miss Cat Irr Pte Pva Ptr N Ptr/N binary AUC 2 0. 78 0. 54 0 0 0 700 100 100 10000 0. 01.
Acknowledgments. We thank the three anonymous referees for their thorough reviews and insightful comments, which have led to considerable improvements in this work. Y. L. is funded by  NSF Climate and Large-Scale Dynamics  ( AGS-1547003 ) and  NASA   JPL  Contract D. W. J. T. is funded by  NSF Climate and Large-Scale Dynamics  ( AGS-1343080  and  AGS-1547003 ). S. B. is funded by the  European Research Council  ( ERC Grant   694768 ). T. M. M. is funded by  Natural Sciences and Engineering Research Council (NSERC)  Grant  RGPIN-2014-05416  and acknowledges a Compute Canada allocation.
RNA from either ripe fruits (ten species) or three ripening stages (two species) as well as leaf RNA (one species) were used to construct, assemble and analyse de novo transcriptomes. The transcriptome sequences are deposited in the BacHBerryGEN database (http://jicbio. nbi. ac. uk/berries) and were used, as a proof of concept, via its BLAST portal (http://jicbio. nbi. ac. uk/berries/blast. html) to identify candidate genes involved in the biosynthesis of phenylpropanoid compounds. Genes encoding regulatory proteins of the anthocyanin biosynthetic pathway (MYB and basic helix-loop-helix (bHLH) transcription factors and WD40 repeat proteins) were isolated using the transcriptomic resources of wild blackberry (Rubus genevieri) and cultivated red raspberry (Rubus idaeus cv. Prestige) and were shown to activate anthocyanin synthesis in Nicotiana benthamiana.
Expression patterns of candidate flavonoid gene transcripts were also studied across three fruit developmental stages via the BacHBerryEXP gene expression browser (http://www. bachberryexp. com) in R. genevieri and R. idaeus cv. Prestige. Conclusions: We report a transcriptome resource that includes data for a wide range of berry(-like) fruit species that has been developed for gene identification and functional analysis to assist in berry fruit improvement. These resources will enable investigations of metabolic processes in berries beyond the phenylpropanoid biosynthetic pathway analysed in this study. The RNA-seq data will be useful for studies of berry fruit development and to select wild plant species useful for plant breeding purposes.
Recent advances in sequencing and computational technologies have greatly facilitated the study of nonmodel, wild and emerging new crop plants and can play key roles in understanding the biosynthetic pathways for novel bioactive compounds. The genetic resources and tools we have developed are available via the web-based transcriptome sequence database BacHBerryGEN  [67]  and its BLAST portal  [68]. Gene expression studies during fruit ripening can be investigated using the newly developed BacHBerryEXP expression browser  [69]  in two Rubus species. As proof of concept, we cloned and conducted the functional analysis of Myb, bHLH and WDR genes involved in regulating anthocyanin biosynthesis in a wild and a cultivated Rubus species, using the transcriptomic tools generated in this study. We also investigated transcript expression patterns of genes involved in flavonoid biosynthesis at three fruit developmental stages in wild blackberry (R.
To allow comparisons to vegetative tissues and due to a predicted high content of polyphenols in leaves, a leaf transcriptome was also prepared for a single species (C. album) for qualitative analysis. The transcriptome datasets are presented in Table  2  and complementary information is provided in Additional file 3: Table  S2  and Additional file 4: Table  S3. The online BacHBerryGEN repository database  [67]  and its BLAST portal  [68]  were developed to allow mining of the transcriptomic data of the 13 wild and cultivated berry fruit species.