Survey on Fall Detection and Fall Prevention Using Wearable and External Sensors <s> Feature Construction <s> This chapter introduces the reader to the various aspects of feature extraction covered in this book. Section 1 reviews definitions and notations and proposes a unified view of the feature extraction problem. Section 2 is an overview of the methods and results presented in the book, emphasizing novel contributions. Section 3 provides the reader with an entry point in the field of feature extraction by showing small revealing examples and describing simple but effective algorithms. Finally, Section 4 introduces a more theoretical formalism and points to directions of research and open problems. <s> BIB001 </s> Survey on Fall Detection and Fall Prevention Using Wearable and External Sensors <s> Feature Construction <s> Feature space design is a critical part of machine learning. This is an especially difficult challenge in the field of text classification, where an arbitrary number of features of varying complexity can be extracted from documents as a preprocessing step. A challenge for researchers has consistently been to balance expressiveness of features with the size of the corresponding feature space, due to issues with data sparsity that arise as feature spaces grow larger. Drawing on past successes utilizing genetic programming in similar problems outside of text classification, we propose and implement a technique for constructing complex features from simpler features, and adding these more complex features into a combined feature space which can then be utilized by more sophisticated machine learning classifiers. Applying this technique to a sentiment analysis problem, we show encouraging improvement in classification accuracy, with a small and constant increase in feature space size. We also show that the features we generate carry far more predictive power than any of the simple features they contain. <s> BIB002
The descriptive power of the feature space is determined by how well the collected data are represented. Feature construction is a key element in the data analysis process, and has a significant impact on subsequent stages of the learning endeavor BIB001 . Realizing an optimal way to represent the input data is domain specific. Most of the time, human experts help in the construction of the feature space. Through their knowledge raw data are converted into meaningful features. Also, their expertise is used to facilitate the task of choosing the adequate variables to observe and not the ones that are irrelevant BIB002 . Feature construction is also considered as a preprocessing step. The collected data goes through a series of transformations that intend to purify (get rid of irrelevant values) the dataset while preserving the essential information BIB001 . Some of the preprocessing transformations are the following: • Standardization. Maximize compatibility among attributes. Feature space size does not suffer a change. • Normalization. Attributes are adjusted to share a common measurement scale. Feature space size suffers no change. • Signal enhancement. Used to improve signal-to-noise ratio. Commonly used in images. Normal operations include: baseline or background removal, de-noising, smoothing, or sharpening. The Fourier and wavelet transforms are widely used. Feature space size does not suffer a change. • Extraction of local features. Attributes are encoded with problem specific knowledge. Feature space size can increase or decrease. • Linear and non-linear embedding methods. Used to reduce the size of the original feature space when it is considerably big without losing excessive information. Principal Component Analysis (PCA) and Multidimensional Scaling (MDS) are frequently used. • Non-linear expansions. Increasing the size of the feature space to ameliorate the complexity. • Feature discretization. Continuous data can be troublesome for some algorithms. It helps in the description and understanding of the data. Feature space is enlarged.