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Title: SECOM Data Set |
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Abstract: Data from a semi-conductor manufacturing process |
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Data Set Characteristics: Multivariate |
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Number of Instances: 1567 |
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Area: Computer |
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Attribute Characteristics: Real |
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Number of Attributes: 591 |
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Date Donated: 2008-11-19 |
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Associated Tasks: Classification, Causal-Discovery |
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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 |
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surveillance via the monitoring of signals/variables collected from sensors and or |
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process measurement points. However, not all of these signals are equally valuable |
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in a specific monitoring system. The measured signals contain a combination of |
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useful information, irrelevant information as well as noise. It is often the case |
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that useful information is buried in the latter two. Engineers typically have a |
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much larger number of signals than are actually required. If we consider each type |
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of signal as a feature, then feature selection may be applied to identify the most |
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relevant signals. The Process Engineers may then use these signals to determine key |
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factors contributing to yield excursions downstream in the process. This will |
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enable an increase in process throughput, decreased time to learning and reduce the |
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per unit production costs. |
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To enhance current business improvement techniques the application of feature |
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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 |
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each example represents a single production entity with associated measured |
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features and the labels represent a simple pass/fail yield for in house line |
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testing, figure 2, and associated date time stamp. Where .1 corresponds to a pass |
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and 1 corresponds to a fail and the data time stamp is for that specific test |
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point. |
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Using feature selection techniques it is desired to rank features according to |
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their impact on the overall yield for the product, causal relationships may also be |
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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 |
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error rates as our evaluation metrics. It is suggested that cross validation be |
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applied to generate these results. Some baseline results are shown below for basic |
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feature selection techniques using a simple kernel ridge classifier and 10 fold |
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cross validation. |
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Baseline Results: Pre-processing objects were applied to the dataset simply to |
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standardize the data and remove the constant features and then a number of |
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different feature selection objects selecting 40 highest ranked features were |
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applied with a simple classifier to achieve some initial results. 10 fold cross |
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validation was used and the balanced error rate (*BER) generated as our initial |
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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 - % |
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S2N (signal to noise) 34.5 +-2.6 57.8 +-5.3 73.1 +2.1 |
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Ttest 33.7 +-2.1 59.6 +-4.7 73.0 +-1.8 |
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Relief 40.1 +-2.8 48.3 +-5.9 71.6 +-3.2 |
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Pearson 34.1 +-2.0 57.4 +-4.3 74.4 +-4.9 |
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Ftest 33.5 +-2.2 59.1 +-4.8 73.8 +-1.8 |
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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 |
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consisting of 1567 examples each with 591 features a 1567 x 591 matrix and a labels |
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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 |
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intensity depending on the individuals features. This needs to be taken into |
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consideration when investigating the data either through pre-processing or within |
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the technique applied. |
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The data is represented in a raw text file each line representing an individual |
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example and the features seperated by spaces. The null values are represented by |
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the 'NaN' value as per MatLab. |
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