| Title: SECOM Data Set |
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| Abstract: Data from a semi-conductor manufacturing process |
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| Data Set Characteristics: Multivariate |
| Number of Instances: 1567 |
| Area: Computer |
| Attribute Characteristics: Real |
| Number of Attributes: 591 |
| Date Donated: 2008-11-19 |
| Associated Tasks: Classification, Causal-Discovery |
| Missing Values? Yes |
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| Source: |
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| Authors: Michael McCann, Adrian Johnston |
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| Data Set Information: |
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| 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. |
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| To enhance current business improvement techniques the application of feature |
| selection as an intelligent systems technique is being investigated. |
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| 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. |
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| 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. |
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| 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. |
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| 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. |
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| SECOM Dataset: 1567 examples 591 features, 104 fails |
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| 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 |
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| Attribute Information: |
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| 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. |
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| 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. |
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| 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. |
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