Improving Cascade Classifier Precision by Instance Selection and Outlier Generation

Abstract

Beside the curse of dimensionality and imbalanced classes, unfavorable data distributions can hamper classification accuracy. This is particularly problematic with increasing dimensionality of the classification task. A classifier that can handle high-dimensional and imbalanced data sets is the cascade classification method for time series. The cascade classifier can compound unfavorable data distributions by projecting the highdimensional data set onto low-dimensional subsets. A classifier is trained for each of the low-dimensional data subsets and their predictions are aggregated to an overall result. For the cascade classifier, the errors of each classifier accumulate in the overall result and therefore small improvements in each small classifier can improve the classification accuracy. Therefore we propose two methods for data preprocessing to improve the cascade classifier. The first method is instance selection, a technique to select representative examples for the classification task. Furthermore, artificial infeasible examples can improve classification performance. Even if high-dimensional infeasible examples are available, their projection to low-dimensional space is not possible due to projection errors. We propose a second data preprocessing method for generating artificial infeasible examples in low-dimensional space. We show for micro Combined Heat and Power plant power production time series and an artificial and complex data set that the proposed data preprocessing methods increase the performance of the cascade classifier by increasing the selectivity of the learned decision boundaries.

Smart Home Anti-Theft System: A Novel Approach for Near Real-Time Monitoring, Smart Home Security and Large Video Data Handling for Wellness Protocol

“…Accurate face detection was achieved in more than 97% of cases, as shown in Figure 16. Secondary Phase: After the initial face detection is complete, an intelligent secondary stage algorithm is designed to eliminate false positives which capture half-hidden human faces, based on the brightest features of the face [24][25][26][27][28][29]. A dynamic threshold was set to capture the brightest part of the face, even where part of the face was hidden.…”

A Review of Classification Problems and Algorithms in Renewable Energy Applications

“…Pattern or prototype selection algorithms are also a widely-used option for BD. • Data imbalance: apart from the above-mentioned learning strategies, prediction models for RE could largely benefit from the use of alternative classification-related techniques [30,111,113]. Imbalanced data are one of the current challenges of ML researchers for classification problems [114], as this poses a serious hindrance for the classification method.…”

Smart Home Anti-Theft System: A Novel Approach for Near Real-Time Monitoring and Smart Home Security for Wellness Protocol

“…After initial face detection is complete, an intelligent secondary stage algorithm was designed to eliminate false positives which capture half-hidden human faces, based on the brightest features of the face [26,27,[33][34][35][36][37][38]. For each positive sample, a dynamic threshold (range from 0.5 to 0.7) was set to capture the brightest part of the intruders partially or fully covered faces.…”