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Please refer to our publications if you use our datasets, tools, results, etc.

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Dissertation

Journal Paper / Book Section

Conference Paper

Workshop / Demo Paper

This page provides an overview of the features that are supported by our Sensor Feature Factory framework. In detail, it covers 10 different features which can be used in context of time- and frequency-domain values. Our Framework uses the JTransforms library to transform the recorded sensor data (time-domain) to the frequency-domain which enables to compute, e.g., the expended energy.

Mean

$$\bar x = \frac{1}{n}*\sum\limits_{i=1}^n x_i$$

Variance

$$var(x) = \frac{1}{n}*\sum\limits_{i=1}^n (\bar x - n_i)^2$$

Standard Deviation

$$\sigma_x = \sqrt{var(x)}$$

Median

$$\widetilde{a}=\begin{cases}x_{\frac{n+1}{2}}&\text{n odd}\\\frac{1}{2}*(x_{\frac{n}{2}}+x_{\frac{n}{2}+1})&\text{$n$ even}\end{cases}$$ $$\text{where}\ \forall x_i,x_j \in X\ x_i\le x_j\ \text{and}\ i\le j$$

Interquartile Range (type R-5)

$$iqr = Q_{0.75}-Q_{0.25}$$ $$Q_p = x_{\lfloor h \rfloor} + (h-\lfloor h \rfloor)(x_{\lfloor h \rfloor+1}-x_{\lfloor h \rfloor})$$ $$h = Np+\frac{1}{2}$$ $$\text{where}\ \forall x_i,x_j \in X\ x_i\le x_j\ \text{and}\ i\le j$$

Mean absolute deviation

$$mad = \frac{1}{n}*\sum\limits_{i=1}^n |x_i - \bar x|$$

Kurtosis

$$w = \frac{1}{n}\sum\limits_{i=1}^n (\frac{x_i - \bar x}{\sigma_x})^4$$

Correlation Coefficient (Pearson)

$$r_{xy} = \frac{\sum\limits_{i=1}^n (x_i - \bar x)(y_i - \bar y)}{\sqrt{\sum\limits_{i=1}^n (x_i - \bar x)^2}*\sqrt{\sum\limits_{i=1}^n (y_i - \bar y)^2}}$$

Entropy (Shannon)

$$IG(S,F) = E(S) - \sum\limits_{v \in Values(F)} \frac{|S_v|}{S}*E(S_v)$$ $$\text{where}\ S_v = \{s \in S | F(s)=v\}$$ $$E(S) = -\sum\limits_{i=1}^{|C|} P(i)*log_2(Pi)$$ $$\text{where}\ P(i)\ \text{ is the fraction of examples in $S$ which is assigned the label $c_i$}$$

Energy (Fourier, Parseval)

$$Energy(Y) = \frac{1}{n}*\sum\limits_{i=1}^{n} F_i^2$$ $$\text{where $F_i$ is the i-th component of the Fourier Transform of Y}$$

This is an implementation of an online random forest classifier in Java. The implementation details and architecture relies on the implementation of Amir Saffari. However, we enhanced the original implementation in several ways.

Quality Measurements

Information Gain, Gini Index

Feature Threshold

random, incremental

Threading

Yes

Export/Import

Coming soon

Java

1.7 and newer (compatible with Android)

DataSet data = new ARFF();
data.load("data.arff");
RandomForest rf = new RandomForest(...)

for (int nEpoch = 0; nEpoch < config.numEpochs; nEpoch++) { // train
  data.randomize();
  for (Sample sample : arffTrain.getSamples()) {
    rf.update(sample);
  }
}

for (Sample sample : arffTest.getSamples()) { // test
  Result result = new Result(data.getNumOfClasses());
  rf.eval(sample, result);
  results.put(result, sample.getLabel());
}                   

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