text: With the emergence of diverse data collection techniques, objects in real applications can be represented as multi-modal features. What's more, objects may have multiple semantic meanings. Multi-modal and Multi-label [1] (MMML) problem becomes a universal phenomenon. The quality of data collected from different channels are inconsistent and some of them may not benefit for prediction. In real life, not all the modalities are needed for prediction. As a result, we propose a novel instance-oriented Multi-modal Classifier Chains (MCC) algorithm for MMML problem, which can make convince prediction with partial modalities. MCC extracts different modalities for different instances in the testing phase. Extensive experiments are performed on one real-world herbs dataset and two public datasets to validate our proposed algorithm, which reveals that it may be better to extract many instead of all of the modalities at hand.
  @cite_14: Parkinson's disease is a debilitating and chronic disease of the nervous system. Traditional Chinese Medicine (TCM) is a new way for diagnosing Parkinson, and the data of Chinese Medicine for diagnosing Parkinson is a multi-label data set. Considering that the symptoms as the labels in Parkinson data set always have correlations with each other, we can facilitate the multi-label learning process by exploiting label correlations. Current multi-label classification methods mainly try to exploit the correlations from label pairwise or label chain. In this paper, we propose a simple and efficient framework for multi-label classification called Latent Dirichlet Allocation Multi-Label (LDAML), which aims at leaning the global correlations by using the topic model on the class labels. Briefly, we try to obtain the abstract “topics” on the label set by topic model, which can exploit the global correlations among the labels. Extensive experiments clearly validate that the proposed approach is a general and effective framework which can improve most of the multi-label algorithms' performance. Based on the framework, we achieve satisfying experimental results on TCM Parkinson data set which can provide a reference and help for the development of this field.
  @cite_1: Parkinson disease is a chronic, degenerative disease of the central nervous system, which commonly occurs in the elderly. Until now, no treatment has shown efficacy. Traditional Chinese Medicine is a new way for Parkinson, and the data of Chinese Medicine for Parkinson is a multi-label dataset. Classifier Chains(CC) is a popular multi-label classification algorithm, this algorithm considers the relativity between labels, and contains the high efficiency of Binary classification algorithm at the same time. But CC algorithm does not indicate how to obtain the predicted order chain actually, while more emphasizes the randomness or artificially specified. In this paper, we try to apply Multi-label classification technology to build a model of Chinese Medicine for Parkinson, which we hope to improve this field. We propose a new algorithm ETCC based on CC model. This algorithm can optimize the order chain on global perspective and have a better result than the algorithm CC.
  @cite_6: The widely known binary relevance method for multi-label classification, which considers each label as an independent binary problem, has often been overlooked in the literature due to the perceived inadequacy of not directly modelling label correlations. Most current methods invest considerable complexity to model interdependencies between labels. This paper shows that binary relevance-based methods have much to offer, and that high predictive performance can be obtained without impeding scalability to large datasets. We exemplify this with a novel classifier chains method that can model label correlations while maintaining acceptable computational complexity. We extend this approach further in an ensemble framework. An extensive empirical evaluation covers a broad range of multi-label datasets with a variety of evaluation metrics. The results illustrate the competitiveness of the chaining method against related and state-of-the-art methods, both in terms of predictive performance and time complexity.
  @cite_13: Many areas of science depend on exploratory data analysis and visualization. The need to analyze large amounts of multivariate data raises the fundamental problem of dimensionality reduction: how to discover compact representations of high-dimensional data. Here, we introduce locally linear embedding (LLE), an unsupervised learning algorithm that computes low-dimensional, neighborhood-preserving embeddings of high-dimensional inputs. Unlike clustering methods for local dimensionality reduction, LLE maps its inputs into a single global coordinate system of lower dimensionality, and its optimizations do not involve local minima. By exploiting the local symmetries of linear reconstructions, LLE is able to learn the global structure of nonlinear manifolds, such as those generated by images of faces or documents of text. How do we judge similarity? Our mental representations of the world are formed by processing large numbers of sensory in
  @cite_11: In classic pattern recognition problems, classes are mutually exclusive by definition. Classification errors occur when the classes overlap in the feature space. We examine a different situation, occurring when the classes are, by definition, not mutually exclusive. Such problems arise in semantic scene and document classification and in medical diagnosis. We present a framework to handle such problems and apply it to the problem of semantic scene classification, where a natural scene may contain multiple objects such that the scene can be described by multiple class labels (e.g., a field scene with a mountain in the background). Such a problem poses challenges to the classic pattern recognition paradigm and demands a different treatment. We discuss approaches for training and testing in this scenario and introduce new metrics for evaluating individual examples, class recall and precision, and overall accuracy. Experiments show that our methods are suitable for scene classification; furthermore, our work appears to generalize to other classification problems of the same nature.
summary: Multi-label learning is a fundamental problem in machine leaning with a wide range of applications. In multi-label learning, each instance is associated with multiple interdependent labels. Binary Relevance (BR) @cite_11 algorithm is the most simple and efficient solution of multi-label algorithms. However, the effectiveness of the resulting approaches might be suboptimal due to the ignorance of label correlations. To tackle this problem, Classifier Chains (CC) @cite_6 was proposed as a high-order approach to consider correlations between labels. It is obviously that the performance of CC is seriously affected by the training order of labels. To account for the effect of ordering, Ensembles of Classifiers Chains (ECC) @cite_6 is an ensemble framework of CC, which can be built with @math random permutation instead of inducing one classifier chain. Entropy Chain Classifier (ETCC) @cite_1 extends CC by calculating the contribution between two labels using information entropy theory while Latent Dirichlet Allocation Multi-Label (LDAML) @cite_14 exploiting global correlations among labels. LDAML mainly solve the problem of large portion of single label instance in some special multi-label datasets. Due to high dimensionality of data , dimensionality reduction @cite_13 or feature extraction should be taken into consideration.