# Adaptive Decision Support System for On-Line Multi-Class Learning and Object Detection
## 1 Introduction
In addition to these improved online learning algorithms inspired by the offline learning algorithm, other algorithms were also proposed to combine with these online learning algorithms, e.g., On-line Conservative Learning, which was proposed by [PERSON], [PERSON]. [17; 18]. In this algorithm, two models are compared, and the higher confidence sample is taken to automatically update the model. It achieves the goal of complete automatic learning and does not need people to label samples. [PERSON] [19] proposed On-line Random Forests, which can be applied in multi-class object detection. It trains each Decision Tree by simulating the number of times the data are sampled, without using Pruning. This is an improved version of Random Forest [13]. However, certain problems remain in the existing online learning algorithms. For example, online learning algorithm is an algorithm for general objects, so there is a huge influence on detection accuracy and learning speed depending on how suitable features are selected for these samples [20; 21]. The online learning algorithms [14; 15; 16], use a single feature during learning. If they use a feature that does not detect a certain object well, for example, using a Haar-like feature [22] to detect a ball-shaped object or LBP [23] to detect a smooth object, the performance suffers. In particular, multi-class classification has this problem because a single feature has insufficient distinguishing ability to distinguish several objects at the same time. Therefore, if we can dynamically allocate the type of feature according to the pattern pool, the problem would be solved. In addition, there are some online learning algorithms such as Online Random Forest [19] whose models are irreversible as soon as the decision has been made. As a result, if the algorithm makes a false decision, the final performance of the detection model declines. At this time, if we can adjust the model thoroughly and correct the false decision, the detection model is able to perform well again. In addition, the classification result would also be bad if the feature that is used cannot effectively classify certain special samples. Most of the existing online learning algorithms, [14; 15; 16] and so on, have this problem. A good solution would be to combine these learning algorithms with an external model according to the status of the model after learning to increase the detection performance of the model. Finally, since the sample of online learning is a one-by-one input, it would also be a challenge to preserve the better distinguished sample. Online Conservative Learning [17; 18] choosesthe sample which confidence as the updating sample is higher to solve this problem. With this approach, however, we cannot determine the more representative sample. Therefore, On-line Learning with Adaptive Model [24] proposes a solution to sample storage and replacement. This method derives the more representative samples by calculating the importance and combining similar samples. For these reasons, this paper proposes an online multi-class learning and detection system that can dynamically allocate features based on samples and can combine with external models based on the learning effect to increase the performance of the model. Therefore, the proposed model not only uses the concept of the sharing feature in Joint Boosting classifier [12] to build the model, but also uses the feature selector in Online Boosting algorithm [15; 16] to update and select features that are distinguished better. Then, we used Back Propagation Neural Network [25] to combine it with the external model. In the pattern pool, the proposed model adopts the same method in On-line Learning with Adaptive Model [24] to make an update. In the feature part, the proposed model uses Haar-like [22], Histogram of Oriented Gradient (HOG) [26], and Local Binary Patterns (LBP) [23].