Can under-exploited structure of original-classes help ECOC-based multi-class classification? Yunyun Wang Songcan Chen Hui Xue School of Computer Science and Engineering, Nanjing University of Aeronautics & Astronautics, 210016, Nanjing, P.R. China School of Computer Science and Engineering, Southeast University, 210096, Nanjing, P.R. China Abstract: Error Correcting Output Codes (ECOC) is a po...

classification ensemble, which uses the weighed polling of outputs, is the art of combining a set of basic classifiers for generating high-performance, robust and more stable results. this study aims to improve the results of identifying the persian handwritten letters using error correcting output coding (ecoc) ensemble method. furthermore, the feature selection is used to reduce the costs of ...

When a sample belongs to more than one label from a set of available classes, the classification problem (known as multi-label classification) turns to be more complicated. Text data, widely available nowadays in the world wide web, is an obvious instance example of such a task. This paper presents a new method for multi-label text categorization created by modifying the Error-Correcting Output...

Previous research has shown that a technique called error-correcting output coding (ECOC) can dramatically improve the classiication accuracy of supervised learning algorithms that learn to classify data points into one of k 2 classes. This paper presents an investigation of why the ECOC technique works, particularly when employed with decision-tree learning algorithms. It shows that the ECOC m...

Output Coding is a method of converting a multiclass problem into several binary subproblems and gives an ensemble of binary classifiers. Like other ensemble methods, its performance depends on the accuracy and diversity of base classifiers. If a decision tree is chosen as base classifier, the issue of tree pruning needs to be addressed. In this paper we investigate the effect of six methods of...

Classification (machine learning): How does one algorithmically classify the though a more effective approach could be using error correcting codes: @(cs/9501101) Solving Multiclass Learning Problems via Error-Correcting Output Codes. to solving machine learning problems can be broadly useful.

Web page prefetching has shown to provide reduction in Web access latency, but is highly dependent on the accuracy of the Web page prediction method. Conditional Random Fields (CRFs) with Error Correcting Output Coding (ECOC) have shown to provide highly accurate and efficient Web page prediction on large-size websites. However, the limited class information provided to the binary-label sub-CRF...

A common way to model multi-class classification problems is by means of Error-Correcting Output Codes (ECOC). Given a multi-class problem, the ECOC technique designs a codeword for each class, where each position of the code identifies the membership of the class for a given binary problem. A classification decision is obtained by assigning the label of the class with the closest code. In this...

Deep neural networks have enhanced the performance of decision making systems in many applications, including image understanding, and further gains can be achieved by constructing ensembles. However, designing an ensemble deep is often not very beneficial since time needed to train generally high or gain obtained significant. In this paper, we analyse error correcting output coding (ECOC) fram...

In volume visualization, the definition of the regions of interest is inherently an iterative trial-and-error process finding out the best parameters to classify and render the final image. Generally, the user requires a lot of expertise to analyze and edit these parameters through multi-dimensional transfer functions. In this paper, we present a framework of intelligent methods to label on-dem...