Binary classification with time-series features2019 Community Moderator ElectionWhat are the best ways to use a time series data for binary classificationTime series classificationbinary longitudinal time seriesBuilding a machine learning model based on a set of timestamped features to predict/classify a label/value?Keras LSTM model for binary classification with sequencesk-Nearest Neighbours with time series data - how to obtain whole-time-period estimatorsTime series binary classificaiton with labelling issuesBinary classification model with time series as variablesMultivariate Time Series Binary Classificationhow to predict content based demandClassification of keystrokes
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Binary classification with time-series features
2019 Community Moderator ElectionWhat are the best ways to use a time series data for binary classificationTime series classificationbinary longitudinal time seriesBuilding a machine learning model based on a set of timestamped features to predict/classify a label/value?Keras LSTM model for binary classification with sequencesk-Nearest Neighbours with time series data - how to obtain whole-time-period estimatorsTime series binary classificaiton with labelling issuesBinary classification model with time series as variablesMultivariate Time Series Binary Classificationhow to predict content based demandClassification of keystrokes
$begingroup$
I have the following time-series features: Diastolic Blood Pressure, Systolic Blood Pressure, Heart Rate, RR variability and Arterial Blood Pressure. Each of these clinical parameters was measured for 900 seconds during a surgical procedure and after the surgery, the patient was assessed for acute kidney injury: 1(yes) or 0(no).
My training data kind of looks like this: (see below for screenshot)
Patient 1 Time(s) Features AKI
Patient 2 Time(s) Features AKI
and so on.
What approach would I take to utilize this data for the binary classification task?
classification time-series
asked Mar 20 at 21:10
John SpanosJohn Spanos
12
$endgroup$
add a comment |
$begingroup$
I have the following time-series features: Diastolic Blood Pressure, Systolic Blood Pressure, Heart Rate, RR variability and Arterial Blood Pressure. Each of these clinical parameters was measured for 900 seconds during a surgical procedure and after the surgery, the patient was assessed for acute kidney injury: 1(yes) or 0(no).
My training data kind of looks like this: (see below for screenshot)
Patient 1 Time(s) Features AKI
Patient 2 Time(s) Features AKI
and so on.
What approach would I take to utilize this data for the binary classification task?
classification time-series
asked Mar 20 at 21:10
John SpanosJohn Spanos
12
$endgroup$
$begingroup$
try lstm for your ptoblem
$endgroup$
– Andreas Look
Mar 20 at 21:23
$begingroup$
Is there any machine-learning approaches to this? Not too familiar with RNNs.
$endgroup$
– John Spanos
Mar 20 at 21:41
$begingroup$
Somehow related question / answer datascience.stackexchange.com/a/25518/29781
$endgroup$
– aivanov
Mar 22 at 13:52
add a comment |
$begingroup$
I have the following time-series features: Diastolic Blood Pressure, Systolic Blood Pressure, Heart Rate, RR variability and Arterial Blood Pressure. Each of these clinical parameters was measured for 900 seconds during a surgical procedure and after the surgery, the patient was assessed for acute kidney injury: 1(yes) or 0(no).
My training data kind of looks like this: (see below for screenshot)
Patient 1 Time(s) Features AKI
Patient 2 Time(s) Features AKI
and so on.
What approach would I take to utilize this data for the binary classification task?
classification time-series
asked Mar 20 at 21:10
John SpanosJohn Spanos
12
$endgroup$
I have the following time-series features: Diastolic Blood Pressure, Systolic Blood Pressure, Heart Rate, RR variability and Arterial Blood Pressure. Each of these clinical parameters was measured for 900 seconds during a surgical procedure and after the surgery, the patient was assessed for acute kidney injury: 1(yes) or 0(no).
My training data kind of looks like this: (see below for screenshot)
Patient 1 Time(s) Features AKI
Patient 2 Time(s) Features AKI
and so on.
What approach would I take to utilize this data for the binary classification task?
classification time-series
classification time-series
asked Mar 20 at 21:10
John SpanosJohn Spanos
12
asked Mar 20 at 21:10
John SpanosJohn Spanos
12
edited Mar 20 at 21:19
John Spanos
asked Mar 20 at 21:10
John SpanosJohn Spanos
12
asked Mar 20 at 21:10
John SpanosJohn Spanos
12
asked Mar 20 at 21:10
John SpanosJohn Spanos
12
12
$begingroup$
try lstm for your ptoblem
$endgroup$
– Andreas Look
Mar 20 at 21:23
$begingroup$
Is there any machine-learning approaches to this? Not too familiar with RNNs.
$endgroup$
– John Spanos
Mar 20 at 21:41
$begingroup$
Somehow related question / answer datascience.stackexchange.com/a/25518/29781
$endgroup$
– aivanov
Mar 22 at 13:52
add a comment |
$begingroup$
try lstm for your ptoblem
$endgroup$
– Andreas Look
Mar 20 at 21:23
$begingroup$
Is there any machine-learning approaches to this? Not too familiar with RNNs.
$endgroup$
– John Spanos
Mar 20 at 21:41
$begingroup$
Somehow related question / answer datascience.stackexchange.com/a/25518/29781
$endgroup$
– aivanov
Mar 22 at 13:52
$begingroup$
try lstm for your ptoblem
$endgroup$
– Andreas Look
Mar 20 at 21:23
$begingroup$
try lstm for your ptoblem
$endgroup$
– Andreas Look
Mar 20 at 21:23
$begingroup$
Is there any machine-learning approaches to this? Not too familiar with RNNs.
$endgroup$
– John Spanos
Mar 20 at 21:41
$begingroup$
Is there any machine-learning approaches to this? Not too familiar with RNNs.
$endgroup$
– John Spanos
Mar 20 at 21:41
$begingroup$
Somehow related question / answer datascience.stackexchange.com/a/25518/29781
$endgroup$
– aivanov
Mar 22 at 13:52
$begingroup$
Somehow related question / answer datascience.stackexchange.com/a/25518/29781
$endgroup$
– aivanov
Mar 22 at 13:52
add a comment |
1 Answer
1
active
oldest
votes
$begingroup$
I would plot the measurements time $t$ and the corresponding measurement 5 to 10 samples for each category. Try to detect some patterns. Possible patterns are the trend (Is the cure growing? Is it linearly? Exponentially?), frequencies of oscillations (Does one category have oscillations with higher magnitude or frequencies? You can use Fast Fourier Transform for this) self similarity of signal (autocorrelation) Then look at means, median, standard deviation, skewness and kurtosis of your signals.
After having extracted all these features I would try to calculate the correlations of your features with the target variable. Then you can eliminate variables that are not very highly correlated with your target variable. In the next step, I would look at the correlations between your features and eliminate the variables that are highly correlated by eliminating the one variable which less correlated with the target variable. Then I would use some classical binary classifiers like discriminant analysis or logistic regression.
If you see that this method will not lead to sufficient results then you should try more sophisticated methods like neural networks/decision trees for the features that you extracted.
answered Mar 20 at 21:44
MachineLearnerMachineLearner
34410
$endgroup$
$begingroup$
Hi, thanks for the answer. To clarify, the target variable isn't numerical. It is a categorical value: either the patient has aki or not.
$endgroup$
– John Spanos
Mar 20 at 23:08
$begingroup$
My explanation is still applicable for these cases. The correlation will be the point-biserial correlation. Everything else can be applied as proposed.
$endgroup$
– MachineLearner
Mar 21 at 7:10
add a comment |
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1 Answer
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oldest
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1 Answer
1
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oldest
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$begingroup$
I would plot the measurements time $t$ and the corresponding measurement 5 to 10 samples for each category. Try to detect some patterns. Possible patterns are the trend (Is the cure growing? Is it linearly? Exponentially?), frequencies of oscillations (Does one category have oscillations with higher magnitude or frequencies? You can use Fast Fourier Transform for this) self similarity of signal (autocorrelation) Then look at means, median, standard deviation, skewness and kurtosis of your signals.
After having extracted all these features I would try to calculate the correlations of your features with the target variable. Then you can eliminate variables that are not very highly correlated with your target variable. In the next step, I would look at the correlations between your features and eliminate the variables that are highly correlated by eliminating the one variable which less correlated with the target variable. Then I would use some classical binary classifiers like discriminant analysis or logistic regression.
If you see that this method will not lead to sufficient results then you should try more sophisticated methods like neural networks/decision trees for the features that you extracted.
answered Mar 20 at 21:44
MachineLearnerMachineLearner
34410
$endgroup$
$begingroup$
Hi, thanks for the answer. To clarify, the target variable isn't numerical. It is a categorical value: either the patient has aki or not.
$endgroup$
– John Spanos
Mar 20 at 23:08
$begingroup$
My explanation is still applicable for these cases. The correlation will be the point-biserial correlation. Everything else can be applied as proposed.
$endgroup$
– MachineLearner
Mar 21 at 7:10
add a comment |
$begingroup$
I would plot the measurements time $t$ and the corresponding measurement 5 to 10 samples for each category. Try to detect some patterns. Possible patterns are the trend (Is the cure growing? Is it linearly? Exponentially?), frequencies of oscillations (Does one category have oscillations with higher magnitude or frequencies? You can use Fast Fourier Transform for this) self similarity of signal (autocorrelation) Then look at means, median, standard deviation, skewness and kurtosis of your signals.
After having extracted all these features I would try to calculate the correlations of your features with the target variable. Then you can eliminate variables that are not very highly correlated with your target variable. In the next step, I would look at the correlations between your features and eliminate the variables that are highly correlated by eliminating the one variable which less correlated with the target variable. Then I would use some classical binary classifiers like discriminant analysis or logistic regression.
If you see that this method will not lead to sufficient results then you should try more sophisticated methods like neural networks/decision trees for the features that you extracted.
answered Mar 20 at 21:44
MachineLearnerMachineLearner
34410
$endgroup$
$begingroup$
Hi, thanks for the answer. To clarify, the target variable isn't numerical. It is a categorical value: either the patient has aki or not.
$endgroup$
– John Spanos
Mar 20 at 23:08
$begingroup$
My explanation is still applicable for these cases. The correlation will be the point-biserial correlation. Everything else can be applied as proposed.
$endgroup$
– MachineLearner
Mar 21 at 7:10
add a comment |
$begingroup$
I would plot the measurements time $t$ and the corresponding measurement 5 to 10 samples for each category. Try to detect some patterns. Possible patterns are the trend (Is the cure growing? Is it linearly? Exponentially?), frequencies of oscillations (Does one category have oscillations with higher magnitude or frequencies? You can use Fast Fourier Transform for this) self similarity of signal (autocorrelation) Then look at means, median, standard deviation, skewness and kurtosis of your signals.
After having extracted all these features I would try to calculate the correlations of your features with the target variable. Then you can eliminate variables that are not very highly correlated with your target variable. In the next step, I would look at the correlations between your features and eliminate the variables that are highly correlated by eliminating the one variable which less correlated with the target variable. Then I would use some classical binary classifiers like discriminant analysis or logistic regression.
If you see that this method will not lead to sufficient results then you should try more sophisticated methods like neural networks/decision trees for the features that you extracted.
answered Mar 20 at 21:44
MachineLearnerMachineLearner
34410
$endgroup$
I would plot the measurements time $t$ and the corresponding measurement 5 to 10 samples for each category. Try to detect some patterns. Possible patterns are the trend (Is the cure growing? Is it linearly? Exponentially?), frequencies of oscillations (Does one category have oscillations with higher magnitude or frequencies? You can use Fast Fourier Transform for this) self similarity of signal (autocorrelation) Then look at means, median, standard deviation, skewness and kurtosis of your signals.
After having extracted all these features I would try to calculate the correlations of your features with the target variable. Then you can eliminate variables that are not very highly correlated with your target variable. In the next step, I would look at the correlations between your features and eliminate the variables that are highly correlated by eliminating the one variable which less correlated with the target variable. Then I would use some classical binary classifiers like discriminant analysis or logistic regression.
If you see that this method will not lead to sufficient results then you should try more sophisticated methods like neural networks/decision trees for the features that you extracted.
answered Mar 20 at 21:44
MachineLearnerMachineLearner
34410
edited Mar 20 at 21:50
answered Mar 20 at 21:44
MachineLearnerMachineLearner
34410
answered Mar 20 at 21:44
MachineLearnerMachineLearner
34410
answered Mar 20 at 21:44
MachineLearnerMachineLearner
34410
34410
$begingroup$
Hi, thanks for the answer. To clarify, the target variable isn't numerical. It is a categorical value: either the patient has aki or not.
$endgroup$
– John Spanos
Mar 20 at 23:08
$begingroup$
My explanation is still applicable for these cases. The correlation will be the point-biserial correlation. Everything else can be applied as proposed.
$endgroup$
– MachineLearner
Mar 21 at 7:10
add a comment |
$begingroup$
Hi, thanks for the answer. To clarify, the target variable isn't numerical. It is a categorical value: either the patient has aki or not.
$endgroup$
– John Spanos
Mar 20 at 23:08
$begingroup$
My explanation is still applicable for these cases. The correlation will be the point-biserial correlation. Everything else can be applied as proposed.
$endgroup$
– MachineLearner
Mar 21 at 7:10
$begingroup$
Hi, thanks for the answer. To clarify, the target variable isn't numerical. It is a categorical value: either the patient has aki or not.
$endgroup$
– John Spanos
Mar 20 at 23:08
$begingroup$
Hi, thanks for the answer. To clarify, the target variable isn't numerical. It is a categorical value: either the patient has aki or not.
$endgroup$
– John Spanos
Mar 20 at 23:08
$begingroup$
My explanation is still applicable for these cases. The correlation will be the point-biserial correlation. Everything else can be applied as proposed.
$endgroup$
– MachineLearner
Mar 21 at 7:10
$begingroup$
My explanation is still applicable for these cases. The correlation will be the point-biserial correlation. Everything else can be applied as proposed.
$endgroup$
– MachineLearner
Mar 21 at 7:10
add a comment |
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$begingroup$
try lstm for your ptoblem
$endgroup$
– Andreas Look
Mar 20 at 21:23
$begingroup$
Is there any machine-learning approaches to this? Not too familiar with RNNs.
$endgroup$
– John Spanos
Mar 20 at 21:41
$begingroup$
Somehow related question / answer datascience.stackexchange.com/a/25518/29781
$endgroup$
– aivanov
Mar 22 at 13:52