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Secom-test

Secom-test

in_preparation ARFF Publicly available Visibility: public Uploaded 20-06-2017 by Stefan Coors
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Source: Authors: Michael McCann, Adrian Johnston Data Set Information: A complex modern semi-conductor manufacturing process is normally under consistent surveillance via the monitoring of signals/variables collected from sensors and or process measurement points. However, not all of these signals are equally valuable in a specific monitoring system. The measured signals contain a combination of useful information, irrelevant information as well as noise. It is often the case that useful information is buried in the latter two. Engineers typically have a much larger number of signals than are actually required. If we consider each type of signal as a feature, then feature selection may be applied to identify the most relevant signals. The Process Engineers may then use these signals to determine key factors contributing to yield excursions downstream in the process. This will enable an increase in process throughput, decreased time to learning and reduce the per unit production costs. To enhance current business improvement techniques the application of feature selection as an intelligent systems technique is being investigated. The dataset presented in this case represents a selection of such features where each example represents a single production entity with associated measured features and the labels represent a simple pass/fail yield for in house line testing, figure 2, and associated date time stamp. Where –1 corresponds to a pass and 1 corresponds to a fail and the data time stamp is for that specific test point. Using feature selection techniques it is desired to rank features according to their impact on the overall yield for the product, causal relationships may also be considered with a view to identifying the key features. Results may be submitted in terms of feature relevance for predictability using error rates as our evaluation metrics. It is suggested that cross validation be applied to generate these results. Some baseline results are shown below for basic feature selection techniques using a simple kernel ridge classifier and 10 fold cross validation. Baseline Results: Pre-processing objects were applied to the dataset simply to standardize the data and remove the constant features and then a number of different feature selection objects selecting 40 highest ranked features were applied with a simple classifier to achieve some initial results. 10 fold cross validation was used and the balanced error rate (*BER) generated as our initial performance metric to help investigate this dataset. SECOM Dataset: 1567 examples 591 features, 104 fails FSmethod (40 features) BER % True + % True - % S2N (signal to noise) 34.5 +-2.6 57.8 +-5.3 73.1 +2.1 Ttest 33.7 +-2.1 59.6 +-4.7 73.0 +-1.8 Relief 40.1 +-2.8 48.3 +-5.9 71.6 +-3.2 Pearson 34.1 +-2.0 57.4 +-4.3 74.4 +-4.9 Ftest 33.5 +-2.2 59.1 +-4.8 73.8 +-1.8 Gram Schmidt 35.6 +-2.4 51.2 +-11.8 77.5 +-2.3 Attribute Information: Key facts: Data Structure: The data consists of 2 files the dataset file SECOM consisting of 1567 examples each with 591 features a 1567 x 591 matrix and a labels file containing the classifications and date time stamp for each example. As with any real life data situations this data contains null values varying in intensity depending on the individuals features. This needs to be taken into consideration when investigating the data either through pre-processing or within the technique applied. The data is represented in a raw text file each line representing an individual example and the features seperated by spaces. The null values are represented by the 'NaN' value as per MatLab. #autoxgboost #autoweka

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a_242numeric1 unique values
0 missing
a_498numeric1 unique values
0 missing
a_243numeric1 unique values
0 missing
a_499numeric193 unique values
0 missing
a_244numeric42 unique values
309 missing
a_500numeric183 unique values
0 missing
a_245numeric161 unique values
309 missing
a_501numeric1 unique values
0 missing
a_246numeric159 unique values
309 missing
a_502numeric1 unique values
0 missing
a_247numeric197 unique values
216 missing
a_503numeric1 unique values
0 missing
a_248numeric266 unique values
0 missing
a_504numeric1 unique values
0 missing
a_249numeric5 unique values
0 missing
a_505numeric1 unique values
0 missing
a_250numeric470 unique values
0 missing
a_506numeric1 unique values
0 missing
a_251numeric63 unique values
0 missing
a_507numeric1 unique values
0 missing
a_252numeric466 unique values
0 missing
a_508numeric1 unique values
0 missing
a_253numeric265 unique values
8 missing
a_509numeric1 unique values
0 missing
a_254numeric209 unique values
0 missing
a_510numeric463 unique values
0 missing
a_255numeric444 unique values
0 missing
a_511numeric192 unique values
0 missing

62 properties

470
Number of instances (rows) of the dataset.
591
Number of attributes (columns) of the dataset.
Number of distinct values of the target attribute (if it is nominal).
12298
Number of missing values in the dataset.
470
Number of instances with at least one value missing.
590
Number of numeric attributes.
1
Number of nominal attributes.
0.35
First quartile of skewness among attributes of the numeric type.
87.26
Mean of means among attributes of the numeric type.
0
First quartile of standard deviation of attributes of the numeric type.
Average class difference between consecutive instances.
Average mutual information between the nominal attributes and the target attribute.
Second quartile (Median) of entropy among attributes.
Entropy of the target attribute values.
An estimate of the amount of irrelevant information in the attributes regarding the class. Equals (MeanAttributeEntropy - MeanMutualInformation) divided by MeanMutualInformation.
6.09
Second quartile (Median) of kurtosis among attributes of the numeric type.
1.26
Number of attributes divided by the number of instances.
2
Average number of distinct values among the attributes of the nominal type.
0.79
Second quartile (Median) of means among attributes of the numeric type.
Number of attributes needed to optimally describe the class (under the assumption of independence among attributes). Equals ClassEntropy divided by MeanMutualInformation.
4.23
Mean skewness among attributes of the numeric type.
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
Percentage of instances belonging to the most frequent class.
59.73
Mean standard deviation of attributes of the numeric type.
1.6
Second quartile (Median) of skewness among attributes of the numeric type.
Number of instances belonging to the most frequent class.
Minimal entropy among attributes.
0.17
Percentage of binary attributes.
0.18
Second quartile (Median) of standard deviation of attributes of the numeric type.
Maximum entropy among attributes.
-1.44
Minimum kurtosis among attributes of the numeric type.
100
Percentage of instances having missing values.
Third quartile of entropy among attributes.
470
Maximum kurtosis among attributes of the numeric type.
-5620.88
Minimum of means among attributes of the numeric type.
4.43
Percentage of missing values.
59.26
Third quartile of kurtosis among attributes of the numeric type.
8820.05
Maximum of means among attributes of the numeric type.
Minimal mutual information between the nominal attributes and the target attribute.
99.83
Percentage of numeric attributes.
10.5
Third quartile of means among attributes of the numeric type.
Maximum mutual information between the nominal attributes and the target attribute.
2
The minimal number of distinct values among attributes of the nominal type.
0.17
Percentage of nominal attributes.
Third quartile of mutual information between the nominal attributes and the target attribute.
2
The maximum number of distinct values among attributes of the nominal type.
-14.7
Minimum skewness among attributes of the numeric type.
First quartile of entropy among attributes.
5.52
Third quartile of skewness among attributes of the numeric type.
21.68
Maximum skewness among attributes of the numeric type.
0
Minimum standard deviation of attributes of the numeric type.
0.57
First quartile of kurtosis among attributes of the numeric type.
6.02
Third quartile of standard deviation of attributes of the numeric type.
6973.77
Maximum standard deviation of attributes of the numeric type.
Percentage of instances belonging to the least frequent class.
Number of instances belonging to the least frequent class.
0
First quartile of means among attributes of the numeric type.
0
Standard deviation of the number of distinct values among attributes of the nominal type.
Average entropy of the attributes.
1
Number of binary attributes.
First quartile of mutual information between the nominal attributes and the target attribute.
67.85
Mean kurtosis among attributes of the numeric type.

15 tasks

0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
0 runs - estimation_procedure: Interleaved Test then Train - 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
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