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micro-mass

micro-mass

active ARFF Publicly available Visibility: public Uploaded 01-06-2015 by Rafael Gomes Mantovani
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Author: Pierre Mahé, Jean-Baptiste Veyrieras Source: UCI Please cite: * Dataset Title: MicroMass - Mixed (mixed spectra version) * Abstract: A dataset to explore machine learning approaches for the identification of microorganisms from mass-spectrometry data. * Source: Pierre Mahé, pierre.mahe '@' biomerieux.com, bioMérieux Jean-Baptiste Veyrieras, jean-baptiste.veyrieras '@' biomerieux.com, bioMérieux * Data Set Information: This MALDI-TOF dataset consists in: A) A reference panel of 20 Gram positive and negative bacterial species covering 9 genera among which several species are known to be hard to discriminate by mass spectrometry (MALDI-TOF). Each species was represented by 11 to 60 mass spectra obtained from 7 to 20 bacterial strains, constituting altogether a dataset of 571 spectra obtained from 213 strains. The spectra were obtained according to the standard culture-based workflow used in clinical routine in which the microorganism was first grown on an agar plate for 24 to 48 hours, before a portion of colony was picked, spotted on a MALDI slide and a mass spectrum was acquired. B) Based on this reference panel, a dedicated in vitro mock-up mixture dataset was constituted. For that purpose we considered 10 pairs of species of various taxonomic proximity: * 4 mixtures, labelled A, B, C and D, involved species that belong to the same genus, * 2 mixtures, labelled E and F, involved species that belong to distinct genera, but to the same Gram type, * 4 mixtures, labelled G, H, I and J, involved species that belong to distinct Gram types. Each mixture was represented by 2 pairs of strains, which were mixed according to the following 9 concentration ratios : 1:0, 10:1, 5:1, 2:1, 1:1, 1:2, 1:5, 1:10, 0:1. Two replicate spectra were acquired for each concentration ratio and each couple of strains, leading altogether to a dataset of 360 spectra, among which 80 are actually pure sample spectra. * Relevant Papers: Mahé et al. (2014). Automatic identification of mixed bacterial species fingerprints in a MALDI-TOF mass-spectrum. Bioinformatics. Vervier et al., A benchmark of support vector machines strategies for microbial identification by mass-spectrometry data, submitted

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0 missing
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0 missing
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0 missing
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0 missing
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0 missing
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0 missing
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0 missing
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0 missing
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0 missing
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0 missing
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0 missing
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0 missing
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0 missing
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0 missing
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0 missing
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0 missing
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V1175numeric22 unique values
0 missing
V152numeric152 unique values
0 missing
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0 missing
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0 missing
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0 missing
V153numeric65 unique values
0 missing
V409numeric95 unique values
0 missing
V665numeric54 unique values
0 missing
V921numeric4 unique values
0 missing
V1177numeric33 unique values
0 missing
V154numeric69 unique values
0 missing
V410numeric23 unique values
0 missing
V666numeric35 unique values
0 missing
V922numeric28 unique values
0 missing
V1178numeric6 unique values
0 missing
V155numeric7 unique values
0 missing
V411numeric3 unique values
0 missing
V667numeric13 unique values
0 missing
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0 missing
V1179numeric41 unique values
0 missing
V156numeric30 unique values
0 missing
V412numeric40 unique values
0 missing
V668numeric1 unique values
0 missing
V924numeric1 unique values
0 missing
V1180numeric61 unique values
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V157numeric71 unique values
0 missing
V413numeric1 unique values
0 missing
V669numeric1 unique values
0 missing
V925numeric53 unique values
0 missing
V1181numeric188 unique values
0 missing
V158numeric71 unique values
0 missing
V414numeric159 unique values
0 missing
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0 missing
V926numeric1 unique values
0 missing
V1182numeric34 unique values
0 missing
V159numeric83 unique values
0 missing
V415numeric118 unique values
0 missing
V671numeric153 unique values
0 missing
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0 missing
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0 missing
V160numeric1 unique values
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0 missing
V672numeric73 unique values
0 missing
V928numeric1 unique values
0 missing
V1184numeric53 unique values
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0 missing
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0 missing
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0 missing
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0 missing
V1185numeric18 unique values
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V162numeric2 unique values
0 missing
V418numeric70 unique values
0 missing
V674numeric30 unique values
0 missing
V930numeric1 unique values
0 missing
V1186numeric7 unique values
0 missing
V163numeric5 unique values
0 missing
V419numeric1 unique values
0 missing
V675numeric48 unique values
0 missing
V931numeric1 unique values
0 missing
V1187numeric15 unique values
0 missing
V164numeric10 unique values
0 missing
V420numeric77 unique values
0 missing
V676numeric1 unique values
0 missing
V932numeric6 unique values
0 missing
V1188numeric59 unique values
0 missing
V165numeric3 unique values
0 missing
V421numeric31 unique values
0 missing
V677numeric15 unique values
0 missing
V933numeric1 unique values
0 missing
V1189numeric22 unique values
0 missing
V166numeric73 unique values
0 missing
V422numeric132 unique values
0 missing
V678numeric100 unique values
0 missing
V934numeric1 unique values
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V1190numeric1 unique values
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V167numeric33 unique values
0 missing
V423numeric96 unique values
0 missing
V679numeric25 unique values
0 missing
V935numeric80 unique values
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V1191numeric9 unique values
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V168numeric1 unique values
0 missing
V424numeric34 unique values
0 missing
V680numeric1 unique values
0 missing
V936numeric59 unique values
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V1192numeric87 unique values
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V169numeric33 unique values
0 missing
V425numeric89 unique values
0 missing
V681numeric80 unique values
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0 missing
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0 missing
V170numeric44 unique values
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V426numeric5 unique values
0 missing
V682numeric118 unique values
0 missing
V938numeric89 unique values
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V1194numeric1 unique values
0 missing
V171numeric171 unique values
0 missing
V427numeric4 unique values
0 missing
V683numeric1 unique values
0 missing
V939numeric17 unique values
0 missing
V1195numeric74 unique values
0 missing
V172numeric43 unique values
0 missing
V428numeric1 unique values
0 missing
V684numeric127 unique values
0 missing
V940numeric50 unique values
0 missing
V1196numeric1 unique values
0 missing
V173numeric54 unique values
0 missing
V429numeric1 unique values
0 missing
V685numeric63 unique values
0 missing
V941numeric15 unique values
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V1197numeric26 unique values
0 missing
V174numeric53 unique values
0 missing
V430numeric193 unique values
0 missing
V686numeric61 unique values
0 missing
V942numeric70 unique values
0 missing
V1198numeric28 unique values
0 missing
V175numeric7 unique values
0 missing
V431numeric4 unique values
0 missing
V687numeric1 unique values
0 missing
V943numeric45 unique values
0 missing
V1199numeric1 unique values
0 missing
V176numeric66 unique values
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V432numeric6 unique values
0 missing
V688numeric51 unique values
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V944numeric4 unique values
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V1200numeric86 unique values
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V433numeric3 unique values
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0 missing
V945numeric57 unique values
0 missing
V1201numeric1 unique values
0 missing
V178numeric171 unique values
0 missing
V434numeric3 unique values
0 missing
V690numeric10 unique values
0 missing
V946numeric1 unique values
0 missing
V1202numeric5 unique values
0 missing
V179numeric1 unique values
0 missing
V435numeric39 unique values
0 missing
V691numeric1 unique values
0 missing
V947numeric44 unique values
0 missing
V1203numeric1 unique values
0 missing
V180numeric47 unique values
0 missing
V436numeric1 unique values
0 missing
V692numeric43 unique values
0 missing
V948numeric41 unique values
0 missing
V1204numeric22 unique values
0 missing
V181numeric4 unique values
0 missing
V437numeric7 unique values
0 missing
V693numeric131 unique values
0 missing
V949numeric7 unique values
0 missing
V1205numeric73 unique values
0 missing
V182numeric1 unique values
0 missing
V438numeric12 unique values
0 missing
V694numeric100 unique values
0 missing
V950numeric67 unique values
0 missing
V1206numeric10 unique values
0 missing
V183numeric4 unique values
0 missing
V439numeric20 unique values
0 missing
V695numeric240 unique values
0 missing
V951numeric5 unique values
0 missing
V1207numeric1 unique values
0 missing
V184numeric7 unique values
0 missing
V440numeric17 unique values
0 missing
V696numeric1 unique values
0 missing
V952numeric2 unique values
0 missing
V1208numeric5 unique values
0 missing
V185numeric5 unique values
0 missing
V441numeric112 unique values
0 missing
V697numeric53 unique values
0 missing
V953numeric127 unique values
0 missing
V1209numeric13 unique values
0 missing
V186numeric90 unique values
0 missing
V442numeric38 unique values
0 missing
V698numeric20 unique values
0 missing
V954numeric35 unique values
0 missing
V1210numeric26 unique values
0 missing
V187numeric7 unique values
0 missing
V443numeric6 unique values
0 missing
V699numeric10 unique values
0 missing
V955numeric74 unique values
0 missing
V1211numeric5 unique values
0 missing
V188numeric24 unique values
0 missing
V444numeric93 unique values
0 missing
V700numeric8 unique values
0 missing
V956numeric1 unique values
0 missing
V1212numeric96 unique values
0 missing
V189numeric55 unique values
0 missing
V445numeric11 unique values
0 missing
V701numeric51 unique values
0 missing
V957numeric70 unique values
0 missing
V1213numeric29 unique values
0 missing
V190numeric31 unique values
0 missing
V446numeric177 unique values
0 missing
V702numeric2 unique values
0 missing
V958numeric104 unique values
0 missing
V1214numeric11 unique values
0 missing
V191numeric31 unique values
0 missing
V447numeric4 unique values
0 missing
V703numeric8 unique values
0 missing
V959numeric8 unique values
0 missing
V1215numeric183 unique values
0 missing
V192numeric131 unique values
0 missing
V448numeric2 unique values
0 missing
V704numeric42 unique values
0 missing
V960numeric67 unique values
0 missing
V1216numeric6 unique values
0 missing
V193numeric1 unique values
0 missing
V449numeric72 unique values
0 missing
V705numeric31 unique values
0 missing
V961numeric19 unique values
0 missing
V1217numeric2 unique values
0 missing
V194numeric17 unique values
0 missing
V450numeric3 unique values
0 missing
V706numeric43 unique values
0 missing
V962numeric3 unique values
0 missing
V1218numeric1 unique values
0 missing
V195numeric64 unique values
0 missing
V451numeric27 unique values
0 missing
V707numeric10 unique values
0 missing
V963numeric3 unique values
0 missing
V1219numeric66 unique values
0 missing
V196numeric9 unique values
0 missing
V452numeric11 unique values
0 missing
V708numeric1 unique values
0 missing
V964numeric99 unique values
0 missing

107 properties

360
Number of instances (rows) of the dataset.
1301
Number of attributes (columns) of the dataset.
10
Number of distinct values of the target attribute (if it is nominal).
0
Number of missing values in the dataset.
0
Number of instances with at least one value missing.
1300
Number of numeric attributes.
1
Number of nominal attributes.
0.78
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.11
Kappa coefficient achieved by the landmarker weka.classifiers.trees.DecisionStump
1415279.42
Maximum of means among attributes of the numeric type.
Minimal mutual information between the nominal attributes and the target attribute.
6.74
Second quartile (Median) of skewness among attributes of the numeric type.
0.84
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
3.61
Number of attributes divided by the number of instances.
Maximum mutual information between the nominal attributes and the target attribute.
10
The minimal number of distinct values among attributes of the nominal type.
0
Percentage of binary attributes.
19624.05
Second quartile (Median) of standard deviation of attributes of the numeric type.
0.29
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
Number of attributes needed to optimally describe the class (under the assumption of independence among attributes). Equals ClassEntropy divided by MeanMutualInformation.
10
The maximum number of distinct values among attributes of the nominal type.
1.09
Minimum skewness among attributes of the numeric type.
0
Percentage of instances having missing values.
Third quartile of entropy among attributes.
0.68
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
0.92
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .00001
18.97
Maximum skewness among attributes of the numeric type.
0
Minimum standard deviation of attributes of the numeric type.
0
Percentage of missing values.
127.64
Third quartile of kurtosis among attributes of the numeric type.
0.97
Average class difference between consecutive instances.
0.84
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.21
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .00001
5001259.47
Maximum standard deviation of attributes of the numeric type.
10
Percentage of instances belonging to the least frequent class.
99.92
Percentage of numeric attributes.
29115.72
Third quartile of means among attributes of the numeric type.
0.91
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.DecisionStump -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
0.29
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.77
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .00001
Average entropy of the attributes.
36
Number of instances belonging to the least frequent class.
0.08
Percentage of nominal attributes.
Third quartile of mutual information between the nominal attributes and the target attribute.
0.2
Error rate achieved by the landmarker weka.classifiers.trees.DecisionStump -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
0.68
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.92
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .0001
95.98
Mean kurtosis among attributes of the numeric type.
0.98
Area Under the ROC Curve achieved by the landmarker weka.classifiers.bayes.NaiveBayes
First quartile of entropy among attributes.
10.71
Third quartile of skewness among attributes of the numeric type.
0.77
Kappa coefficient achieved by the landmarker weka.classifiers.trees.DecisionStump -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
0.84
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.21
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .0001
45702.37
Mean of means among attributes of the numeric type.
0.08
Error rate achieved by the landmarker weka.classifiers.bayes.NaiveBayes
24.29
First quartile of kurtosis among attributes of the numeric type.
108883.56
Third quartile of standard deviation of attributes of the numeric type.
0.91
Area Under the ROC Curve achieved by the landmarker weka.classifiers.bayes.NaiveBayes -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
0.29
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.77
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .0001
Average mutual information between the nominal attributes and the target attribute.
0.91
Kappa coefficient achieved by the landmarker weka.classifiers.bayes.NaiveBayes
84.98
First quartile of means among attributes of the numeric type.
0.92
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.2
Error rate achieved by the landmarker weka.classifiers.bayes.NaiveBayes -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
0.68
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.92
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .001
An estimate of the amount of irrelevant information in the attributes regarding the class. Equals (MeanAttributeEntropy - MeanMutualInformation) divided by MeanMutualInformation.
0
Number of binary attributes.
First quartile of mutual information between the nominal attributes and the target attribute.
0.2
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.77
Kappa coefficient achieved by the landmarker weka.classifiers.bayes.NaiveBayes -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
0.91
Area Under the ROC Curve achieved by the landmarker weka.classifiers.lazy.IBk -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
0
Standard deviation of the number of distinct values among attributes of the nominal type.
0.21
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .001
10
Average number of distinct values among the attributes of the nominal type.
4.66
First quartile of skewness among attributes of the numeric type.
0.78
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.2
Error rate achieved by the landmarker weka.classifiers.lazy.IBk -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
0.92
Area Under the ROC Curve achieved by the landmarker weka.classifiers.lazy.IBk
0.77
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .001
8.19
Mean skewness among attributes of the numeric type.
1086.88
First quartile of standard deviation of attributes of the numeric type.
0.92
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.77
Kappa coefficient achieved by the landmarker weka.classifiers.lazy.IBk -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
0.14
Error rate achieved by the landmarker weka.classifiers.lazy.IBk
10
Percentage of instances belonging to the most frequent class.
139708.17
Mean standard deviation of attributes of the numeric type.
Second quartile (Median) of entropy among attributes.
0.2
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 2
3.32
Entropy of the target attribute values.
0.85
Kappa coefficient achieved by the landmarker weka.classifiers.lazy.IBk
36
Number of instances belonging to the most frequent class.
Minimal entropy among attributes.
50.56
Second quartile (Median) of kurtosis among attributes of the numeric type.
0.78
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.92
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.75
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.DecisionStump
Maximum entropy among attributes.
-0.07
Minimum kurtosis among attributes of the numeric type.
3734.59
Second quartile (Median) of means among attributes of the numeric type.
0.2
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.8
Error rate achieved by the landmarker weka.classifiers.trees.DecisionStump
360
Maximum kurtosis among attributes of the numeric type.
0
Minimum of means among attributes of the numeric type.
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.

13 tasks

64 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: Class
0 runs - estimation_procedure: 10-fold Crossvalidation - target_feature: Class
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
Define a new task