Algorithms for Similarity Relation Learning from High Dimensional Data

Abstract

Abstract. The notion of similarity plays an important role in machine learning and artificial intelligence. It is widely used in tasks related to a supervised classification, clustering, an outlier detection and planning. Moreover, in domains such as information retrieval or case-based reasoning, the concept of similarity is essential as it is used at every phase of the reasoning cycle. The similarity itself, however, is a very complex concept that slips out from formal definitions. A similarity of two objects can be different depending on a considered context. In many practical situations it is difficult even to evaluate the quality of similarity assessments without considering the task for which they were performed. Due to this fact the similarity should be learnt from data, specifically for the task at hand. This paper presents a research on the problem of similarity learning, which is a part of author's PHD dissertation. It describes a similarity model, called Rule-Based Similarity, and shows algorithms for constructing this model from available data. The model utilizes notions from the rough set theory to derive a similarity function that allows to approximate the similarity relation in a given context. It is largely inspired by the idea of Tversky's feature contrast model and it has several analogical properties. In the paper, those theoretical properties are described and discussed. Moreover, the paper presents results of experiments on real-life data sets, in which a quality of the proposed model is thoroughly evaluated and compared with the state-of-the-art algorithms.

Identification of Product’s Features Based on Customer Reviews

“…In [12], author proposed a similarity model, called Rule-Based Similarity (RBS). In RBS the similarity is assessed by examining whether two objects share some binary higherlevel features.…”

“…where F : RˆR Ñ R can be any function that is monotonically increasing with regard to its first argument and monotonically decreasing with regard to its second argument. Detailed description of the similarity function is provided in [12]. Figure 2 provides a general overview of the construction of a summary system for the feature extraction.…”

“…As in [12], the quality of the compared models was assessed using two different measuresmean accuracy (Mean) and balanced accuracy (Balanced). The mean classification accuracy, defined as:…”

“…M ean " |ttPDataSet:pptq"dptqu| |DataSet| (12) where DataSet is a set of test objects; pptq is a predication of a decision class for an object t and dptq is an expected decision class for an object t, was estimated using 3-fold crossvalidation technique [18]. The balanced accuracy is calculated by computing standard classification accuracies M ean i for each decision class and then averaging the result over classes: i P tpositive, negativeu [12].…”

“…Rule-Based Similarity Model that is presented in Section VI derives from the theory of rough sets (definitions presented in this section comes from [12]). The theory of rough sets, proposed by Zdzislaw Pawlak in 1981 [13], provides a mathematical formalism for reasoning about imperfect data and knowledge.…”