title: Effective Linear-Time Feature Selection

authors: Pradhananga, Nripendra

subjects: filter

abstract: The classification learning task requires selection of a subset of features to represent patterns
to be classified. This is because the performance of the classifier and the cost of
classification are sensitive to the choice of the features used to construct the classifier.
Exhaustive search is impractical since it searches every possible combination of features.
The runtime of heuristic and random searches are better but the problem still persists
when dealing with high-dimensional datasets.
We investigate a heuristic, forward, wrapper-based approach, called Linear Sequential
Selection, which limits the search space at each iteration of the feature selection process.
We introduce randomization in the search space. The algorithm is called Randomized
Linear Sequential Selection. Our experiments demonstrate that both methods are faster,
find smaller subsets and can even increase the classification accuracy.
We also explore the idea of ensemble learning. We have proposed two ensemble creation
methods, Feature Selection Ensemble and Random Feature Ensemble. Both methods apply
a feature selection algorithm to create individual classifiers of the ensemble. Our
experiments have shown that both methods work well with high-dimensional data.

text: Effective Linear-Time Feature Selection The classification learning task requires selection of a subset of features to represent patterns
to be classified. This is because the performance of the classifier and the cost of
classification are sensitive to the choice of the features used to construct the classifier.
Exhaustive search is impractical since it searches every possible combination of features.
The runtime of heuristic and random searches are better but the problem still persists
when dealing with high-dimensional datasets.
We investigate a heuristic, forward, wrapper-based approach, called Linear Sequential
Selection, which limits the search space at each iteration of the feature selection process.
We introduce randomization in the search space. The algorithm is called Randomized
Linear Sequential Selection. Our experiments demonstrate that both methods are faster,
find smaller subsets and can even increase the classification accuracy.
We also explore the idea of ensemble learning. We have proposed two ensemble creation
methods, Feature Selection Ensemble and Random Feature Ensemble. Both methods apply
a feature selection algorithm to create individual classifiers of the ensemble. Our
experiments have shown that both methods work well with high-dimensional data.

year: 2007

title: Noise considerations when determining phase of large-signal microwave measurements

authors: Blockley, Peter Stuart

subjects: intermodulation distortion

abstract: Advances in microwave instrumentation now make it feasible to accurately measure not only the magnitude spectrum, but also the phase spectrum of wide-bandwidth signals. In a practical measurement, the spectrum is measured over a finite window of time. The phase spectrum is related to the position of this window, causing the spectrum to differ between measurements of an identical waveform. It is difficult to compare multiple measurements with different window positions or to incorporate them into a model. Several methods have been proposed for determining the phase spectrum such that multiple measurements can be effectively compared and utilized in models. The methods are reviewed in terms of the information required to determine the phase and compared in terms of their robustness in the presence of measurement noise.

text: Noise considerations when determining phase of large-signal microwave measurements Advances in microwave instrumentation now make it feasible to accurately measure not only the magnitude spectrum, but also the phase spectrum of wide-bandwidth signals. In a practical measurement, the spectrum is measured over a finite window of time. The phase spectrum is related to the position of this window, causing the spectrum to differ between measurements of an identical waveform. It is difficult to compare multiple measurements with different window positions or to incorporate them into a model. Several methods have been proposed for determining the phase spectrum such that multiple measurements can be effectively compared and utilized in models. The methods are reviewed in terms of the information required to determine the phase and compared in terms of their robustness in the presence of measurement noise.

year: 2006

title: Changing the acculturation conversation: Indigenous cultural reclamation in Australia and Aotearoa/New Zealand

authors: Dudgeon, Pat

abstract: In constructing this chapter the authors make a claim for an Indigenous perspective that is grounded in decolonisation, the struggle for social justice, cultural reclamation and the development of Indigenous kowledges. This offers the opportunity to view acculturation and the associate research through a different lens. In taking this stance, a critical psychology, Indigenous standpoint approach is adopted, while also acknowledging earlier scholars who have attempted to accommodate Indigenous experiences within acculturation theory and the associated research (see for example, Kvernmo, 2006). This chapter begins with a critique of the acculturation framework and practice in researching the acculturation experience of Indigenous peoples. The chapter then presents findings and application to two contexts: Australia and Aotearoa/New Zealand. (Chapter 11 covers acculturation research in Australia and New Zealand and would thus be relevant to this chapter.) How acculturation research can be linked to cultural reclamation and reconciliation work is then examined.

text: Changing the acculturation conversation: Indigenous cultural reclamation in Australia and Aotearoa/New Zealand In constructing this chapter the authors make a claim for an Indigenous perspective that is grounded in decolonisation, the struggle for social justice, cultural reclamation and the development of Indigenous kowledges. This offers the opportunity to view acculturation and the associate research through a different lens. In taking this stance, a critical psychology, Indigenous standpoint approach is adopted, while also acknowledging earlier scholars who have attempted to accommodate Indigenous experiences within acculturation theory and the associated research (see for example, Kvernmo, 2006). This chapter begins with a critique of the acculturation framework and practice in researching the acculturation experience of Indigenous peoples. The chapter then presents findings and application to two contexts: Australia and Aotearoa/New Zealand. (Chapter 11 covers acculturation research in Australia and New Zealand and would thus be relevant to this chapter.) How acculturation research can be linked to cultural reclamation and reconciliation work is then examined.

year: 2016