Imbalanced dataset for benchmarking ======================= The different algorithms of the `imbalanced-learn` toolbox are evaluated on a set of common dataset, which are more or less balanced. These benchmark have been proposed in [1]. The following section presents the main characteristics of this benchmark. Characteristics ------------------- |ID |Name |Repository & Target |Ratio |# samples| # features | |:---:|:----------------------:|--------------------------------------|:------:|:-------------:|:--------------:| |1 |Ecoli |UCI, target: imU |8.6:1 |336 |7 | |2 |Optical Digits |UCI, target: 8 |9.1:1 |5,620 |64 | |3 |SatImage |UCI, target: 4 |9.3:1 |6,435 |36 | |4 |Pen Digits |UCI, target: 5 |9.4:1 |10,992 |16 | |5 |Abalone |UCI, target: 7 |9.7:1 |4,177 |8 | |6 |Sick Euthyroid |UCI, target: sick euthyroid |9.8:1 |3,163 |25 | |7 |Spectrometer |UCI, target: >=44 |11:1 |531 |93 | |8 |Car_Eval_34 |UCI, target: good, v good |12:1 |1,728 |6 | |9 |ISOLET |UCI, target: A, B |12:1 |7,797 |617 | |10 |US Crime |UCI, target: >0.65 |12:1 |1,994 |122 | |11 |Yeast_ML8 |LIBSVM, target: 8 |13:1 |2,417 |103 | |12 |Scene |LIBSVM, target: >one label |13:1 |2,407 |294 | |13 |Libras Move |UCI, target: 1 |14:1 |360 |90 | |14 |Thyroid Sick |UCI, target: sick |15:1 |3,772 |28 | |15 |Coil_2000 |KDD, CoIL, target: minority |16:1 |9,822 |85 | |16 |Arrhythmia |UCI, target: 06 |17:1 |452 |279 | |17 |Solar Flare M0 |UCI, target: M->0 |19:1 |1,389 |10 | |18 |OIL |UCI, target: minority |22:1 |937 |49 | |19 |Car_Eval_4 |UCI, target: vgood |26:1 |1,728 |6 | |20 |Wine Quality |UCI, wine, target: <=4 |26:1 |4,898 |11 | |21 |Letter Img |UCI, target: Z |26:1 |20,000 |16 | |22 |Yeast _ME2 |UCI, target: ME2 |28:1 |1,484 |8 | |23 |Webpage |LIBSVM, w7a, target: minority|33:1 |49,749 |300 | |24 |Ozone Level |UCI, ozone, data |34:1 |2,536 |72 | |25 |Mammography |UCI, target: minority |42:1 |11,183 |6 | |26 |Protein homo. |KDD CUP 2004, minority |111:1|145,751 |74 | |27 |Abalone_19 |UCI, target: 19 |130:1|4,177 |8 | References ---------- [1] Ding, Zejin, "Diversified Ensemble Classifiers for H ighly Imbalanced Data Learning and their Application in Bioinformatics." Dissertation, Georgia State University, (2011). [2] Blake, Catherine, and Christopher J. Merz. "UCI Repository of machine learning databases." (1998). [3] Chang, Chih-Chung, and Chih-Jen Lin. "LIBSVM: a library for support vector machines." ACM Transactions on Intelligent Systems and Technology (TIST) 2.3 (2011): 27. [4] Caruana, Rich, Thorsten Joachims, and Lars Backstrom. "KDD-Cup 2004: results and analysis." ACM SIGKDD Explorations Newsletter 6.2 (2004): 95-108.

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