Learning the attribute selection measures for decision tree

Decision tree has most widely used for classification. However the main influence of decision tree classification performance is attribute selection problem. The paper considers a number of different attribute selection measures and experimentally examines their behavior in classification. The results show that the choice of measure doesn't affect the classification accuracy, but the size of the tree is influenced significantly. The main effect of the new attribute selection measures which base on normal gain and distance is that they generate smaller trees than traditional attribute selection measures. © 2013 SPIE

Learning Interpretable Rules for Multi-label Classification

Multi-label classification (MLC) is a supervised learning problem in which, contrary to standard multiclass classification, an instance can be associated with several class labels simultaneously. In this chapter, we advocate a rule-based approach to multi-label classification. Rule learning algorithms are often employed when one is not only interested in accurate predictions, but also requires an interpretable theory that can be understood, analyzed, and qualitatively evaluated by domain experts. Ideally, by revealing patterns and regularities contained in the data, a rule-based theory yields new insights in the application domain. Recently, several authors have started to investigate how rule-based models can be used for modeling multi-label data. Discussing this task in detail, we highlight some of the problems that make rule learning considerably more challenging for MLC than for conventional classification. While mainly focusing on our own previous work, we also provide a short overview of related work in this area.Comment: Preprint version. To appear in: Explainable and Interpretable Models in Computer Vision and Machine Learning. The Springer Series on Challenges in Machine Learning. Springer (2018). See http://www.ke.tu-darmstadt.de/bibtex/publications/show/3077 for further informatio

Exploiting Anti-monotonicity of Multi-label Evaluation Measures for Inducing Multi-label Rules

Exploiting dependencies between labels is considered to be crucial for multi-label classification. Rules are able to expose label dependencies such as implications, subsumptions or exclusions in a human-comprehensible and interpretable manner. However, the induction of rules with multiple labels in the head is particularly challenging, as the number of label combinations which must be taken into account for each rule grows exponentially with the number of available labels. To overcome this limitation, algorithms for exhaustive rule mining typically use properties such as anti-monotonicity or decomposability in order to prune the search space. In the present paper, we examine whether commonly used multi-label evaluation metrics satisfy these properties and therefore are suited to prune the search space for multi-label heads.Comment: Preprint version. To appear in: Proceedings of the Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD) 2018. See http://www.ke.tu-darmstadt.de/bibtex/publications/show/3074 for further information. arXiv admin note: text overlap with arXiv:1812.0005

Polyphonic music information retrieval based on multi-label cascade classification system

Recognition and separation of sounds played by various instruments is very useful in labeling audio files with semantic information. This is a non-trivial task requiring sound analysis, but the results can aid automatic indexing and browsing music data when searching for melodies played by user specified instruments. Melody match based on pitch detection technology has drawn much attention and a lot of MIR systems have been developed to fulfill this task. However, musical instrument recognition remains an unsolved problem in the domain. Numerous approaches on acoustic feature extraction have already been proposed for timbre recognition. Unfortunately, none of those monophonic timbre estimation algorithms can be successfully applied to polyphonic sounds, which are the more usual cases in the real music world. This has stimulated the research on multi-labeled instrument classification and new features development for content-based automatic music information retrieval. The original audio signals are the large volume of unstructured sequential values, which are not suitable for traditional data mining algorithms; while the acoustical features are sometime not sufficient for instrument recognition in polyphonic sounds because they are higher-level representatives of raw signal lacking details of original information. In order to capture the patterns which evolve on the time scale, new temporal features are introduced to supply more temporal information for the timbre recognition. We will introduce the multi-labeled classification system to estimate multiple timbre information from the polyphonic sound by classification based on acoustic features and short-term power spectrum matching. In order to achieve higher estimation rate, we introduced the hierarchically structured cascade classification system under the inspiration of the human perceptual process. This cascade classification system makes a first estimate on the higher level decision attribute, which stands for the musical instrument family. Then, the further estimation is done within that specific family range. Experiments showed better performance of a hierarchical system than the traditional flat classification method which directly estimates the instrument without higher level of family information analysis. Traditional hierarchical structures were constructed in human semantics, which are meaningful from human perspective but not appropriate for the cascade system. We introduce the new hierarchical instrument schema according to the clustering results of the acoustic features. This new schema better describes the similarity among different instruments or among different playing techniques of the same instrument. The classification results show the higher accuracy of cascade system with the new schema compared to the traditional schemas. The query answering system is built based on the cascade classifier

Multi-label Rule Learning

Research on multi-label classification is concerned with developing and evaluating algorithms that learn a predictive model for the automatic assignment of data points to a subset of predefined class labels. This is in contrast to traditional classification settings, where individual data points cannot be assigned to more than a single class. As many practical use cases demand a flexible categorization of data, where classes must not necessarily be mutually exclusive, multi-label classification has become an established topic of machine learning research. Nowadays, it is used for the assignment of keywords to text documents, the annotation of multimedia files, such as images, videos, or audio recordings, as well as for diverse applications in biology, chemistry, social network analysis, or marketing. During the past decade, increasing interest in the topic has resulted in a wide variety of different multi-label classification methods. Following the principles of supervised learning, they derive a model from labeled training data, which can afterward be used to obtain predictions for yet unseen data. Besides complex statistical methods, such as artificial neural networks, symbolic learning approaches have not only been shown to provide state-of-the-art performance in many applications but are also a common choice in safety-critical domains that demand human-interpretable and verifiable machine learning models. In particular, rule learning algorithms have a long history of active research in the scientific community. They are often argued to meet the requirements of interpretable machine learning due to the human-legible representation of learned knowledge in terms of logical statements. This work presents a modular framework for implementing multi-label rule learning methods. It does not only provide a unified view of existing rule-based approaches to multi-label classification, but also facilitates the development of new learning algorithms. Two novel instantiations of the framework are investigated to demonstrate its flexibility. Whereas the first one relies on traditional rule learning techniques and focuses on interpretability, the second one is based on a generalization of the gradient boosting framework and focuses on predictive performance rather than the simplicity of models. Motivated by the increasing demand for highly scalable learning algorithms that are capable of processing large amounts of training data, this work also includes an extensive discussion of algorithmic optimizations and approximation techniques for the efficient induction of rules. As the novel multi-label classification methods that are presented in this work can be viewed as instantiations of the same framework, they can both benefit from most of these principles. Their effectiveness and efficiency are compared to existing baselines experimentally

LWA 2013. Lernen, Wissen & Adaptivität ; Workshop Proceedings Bamberg, 7.-9. October 2013

LWA Workshop Proceedings: LWA stands for "Lernen, Wissen, Adaption" (Learning, Knowledge, Adaptation). It is the joint forum of four special interest groups of the German Computer Science Society (GI). Following the tradition of the last years, LWA provides a joint forum for experienced and for young researchers, to bring insights to recent trends, technologies and applications, and to promote interaction among the SIGs

Multiple labels associative classification

Building fast and accurate classifiers for large-scale databases is an important task in data mining. There is growing evidence that integrating classification and association rule mining can produce more efficient and accurate classifiers than traditional techniques. In this paper, the problem of producing rules with multiple labels is investigated, and we propose a multi-class, multi-label associative classification approach (MMAC). In addition, four measures are presented in this paper for evaluating the accuracy of classification approaches to a wide range of traditional and multi-label classification problems. Results for 19 different data sets from the UCI data collection and nine hyperheuristic scheduling runs show that the proposed approach is an accurate and effective classification technique, highly competitive and scalable if compared with other traditional and associative classification approaches