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To try to decrease the preference of the attribute values for information gain and information gain ratio, in the paper, the authors puts forward a improved algorithm of C4.5 decision tree on the selection classification attribute. The basic thought of the algorithm is as follows: Firstly, computing the information gain of selection classification attribute, and then get an attribute of the information...
Inner-product operations are used extensively in embedded digital signal processing (DSP) systems. Their applications range from signal processing (filtering/convolution) to inference (classification). In embedded systems, resources (energy, area, etc.) are typically highly constrained, making tradeoffs with computational precision a fundamental concern. Indeed, with increasing requirements on algorithmic...
Large-area electronics (LAE) enables the formation of a large number of sensors capable of spanning dimensions on the order of square meters. An example is X-ray imagers, which have been scaling both in dimension and number of sensors, today reaching millions of pixels. However, processing of the sensor data requires interfacing thousands of signals to CMOS ICs, because implementation of complex functions...
Embedded sensing systems conventionally perform A-to-D conversion followed by signal analysis. In many applications, the analysis of interest is inference (e.g., classification), but the sensor signals involved are too complex to model analytically. Machine learning is gaining prominence because it enables data-driven training of classifiers, overcoming the need for analytical models. This work presents:...
In the paper, put forward classification and discrimination based on rough sets-partial least squares-discriminant analysis (RS-PLS-DA). The method was proved to be feasible and effective after tested with a complication of diabetes database.
In order to classify the disease from the clinical database, according to the characteristic of disease in rough relational databases, a method to classify the disease was put forward. Firstly, to collect the disease symptom model information table from the clinical database; secondly, symptom typical characterizing using accuracy; thirdly, classify the disease based on a novel rough distance. The...
Traditional hierarchical clustering (HC) methods which always fail to deal with very large databases or high dimensional spaces. In the paper, arithmetic mean, arithmetic mean ratios and so on are introduced. Variable trend, data preprocessing for database based on variable trend, and mining VIP (variable important in M/Z, or called typical characteristic) based on an improved hierarchical clustering...
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