Intelligent Data Analysis - Volume 21, issue 3

Authors: Famili, A.

Article Type: Review Article

DOI: 10.3233/IDA-170880

Citation: Intelligent Data Analysis, vol. 21, no. 3, pp. 463-465, 2017

Authors: Schclar, Alon | Rokach, Lior | Amit, Amir

Article Type: Research Article

Abstract: We present a novel approach for the construction of ensemble classifiers based on dimensionality reduction. The ensemble members are trained based on dimension-reduced versions of the training set. In order to classify a test sample, it is first embedded into the dimension reduced space of each individual classifier by using an out-of-sample extension algorithm. Each classifier is then applied to the embedded sample and the classification is obtained via a voting scheme. We demonstrate the proposed approach using the Random Projections, the Diffusion Maps and the Random Subspaces dimensionality reduction algorithms. We also present a multi-strategy ensemble which combines AdaBoost …and Diffusion Maps. A comparison is made with the Bagging, AdaBoost, Rotation Forest ensemble classifiers and also with the base classifier. Our experiments used seventeen benchmark datasets from the UCI repository. The results obtained by the proposed algorithms were superior in many cases to other algorithms. Show more

Keywords: Ensembles of classifiers, dimensionality reduction, out-of-sample extension, Random Projections, Diffusion Maps, Nyström extension

DOI: 10.3233/IDA-150486

Citation: Intelligent Data Analysis, vol. 21, no. 3, pp. 467-489, 2017

Authors: Vinh, Vo Thanh | Anh, Duong Tuan

Article Type: Research Article

Abstract: Research in time series classification has shown that the one nearest neighbor with Dynamic Time Warping measure in most cases outperforms more advanced classification algorithms. Instance reduction is one of the approaches to improve time and space efficiency of nearest neighbor classifier for time series data. This approach reduces the size of the training set by selecting the best representative instances and uses only them during classification of new instances. In this work, we propose a novel approach for instance reduction in time series classification. Our method consists of two steps. First, we remove the unrepresentative instances in the training …set, using data editing. In the second step, we compress the training set using the Minimum Description Length principle. The main idea behind our method is that if we can compress the two time series by the Minimum Description Length principle, we will combine them into one time series. By this way, the number of instances in the training set is reduced step by step, and we stop removing instances from the training set when reaching some required percentage of instances in the training set or when we can not find any pair of instances to compress. We empirically compare our proposed method with the two previous methods, INSIGHT and Naïve Rank Reduction, over a vast majority number of time series training sets. The experimental results show that our method can outperform INSIGHT and Naïve Rank Reduction in many datasets when the percentage of selected instances in the training set is not too small, about greater than 30%. Show more

Keywords: Time series classification, dynamic time warping, instance reduction, MDL principle, compression rate

DOI: 10.3233/IDA-150475

Citation: Intelligent Data Analysis, vol. 21, no. 3, pp. 491-514, 2017

Authors: Hadjadji, Bilal | Chibani, Youcef | Guerbai, Yasmine

Article Type: Research Article

Abstract: One-Class Classifier (OCC) has been widely used for its ability to learn without counterexamples. Its extension for multi-class implementation offers an open scheme which allows easily adding new classes. However, using OCCs for the multi-class implementation usually achieves less accuracy than the usual multi-class implementations. Hence, in order to improve the accuracy and keep the offered advantage, the suitable approach consists to combine different classifiers. Thus, this paper proposes a study of combining different types of OCC for multi-class classification by means of a new Dynamic Weighted Average (DWA) combination rule. Experimental results conducted on several real-world datasets prove the …effective use of the proposed approach where the DWA rule achieves the best results against fixed rules as well as the decision template. Furthermore, comparison of the proposed open classification system against a standard open classifier based on K-Nearest Neighbor (K-NN) shows the superiority of the proposed system. Show more

Keywords: One-class classifiers, multi-class implementation, multiple classifiers system, open multi-class classification, dynamic weighted average combination rule

DOI: 10.3233/IDA-150420

Citation: Intelligent Data Analysis, vol. 21, no. 3, pp. 515-535, 2017

Authors: Vu, Viet-Vu | Labroche, Nicolas

Article Type: Research Article

Abstract: Active learning for semi-supervised clustering allows algorithms to solicit a domain expert to provide side information as instances constraints, for example a set of labeled instances called seeds. The problem consists in selecting the queries to the expert that are likely to improve either the relevance or the quality of the proposed clustering. However, these active methods suffer from several limitations: (i) they are generally tailored for only one specific clustering paradigm or cluster shape and size, (ii) they may be counter-productive if the seeds are not selected in an appropriate manner and, (iii) they have to work efficiently with …minimal expert supervision. In this paper, we propose a new active seed selection algorithm that relies on a k-nearest neighbors structure to locate dense potential clusters and efficiently query and propagate expert information. Our approach makes no hypothesis about the underlying data distribution and can be paired with any clustering algorithm. Comparative experiments conducted on real data sets show the efficiency of this new approach compared to existing ones. Show more

Keywords: Active learning, seed selection, seed based clustering, k-nearest neighbor graph

DOI: 10.3233/IDA-150499

Citation: Intelligent Data Analysis, vol. 21, no. 3, pp. 537-552, 2017

Authors: Ansari, Zahid | Faizabadi, Ahmed Rimaz | Afzal, Asif

Article Type: Research Article

Abstract: Mining web usage data of e-business organizations is essential to provide knowledge about clients’ web utilization patterns, which can help these businesses in landing at vital business choices. Because of non-deterministic web access behavior of web clients, web user session data is usually noisy and imperfect. Such imperfection has a negative impact on pattern discovery process. One of the real issues associated with the prevalently used Fuzzy c-Means (FCM) and Fuzzy c-Medoids (FCMdd) methods is that they are not robust against the noise, because a single outlier object could lead to a very different clustering result. In this research we …propose a robust Fuzzy c-Least Medians (FCLMdn) clustering framework to deal with the user session data contaminated with noise and outlier user session objects, with the objective of improving the quality of the extracted patterns. To deal with the high dimensionality of user session data which may contain noise and outliers, a fuzzy set theoretic approach for assigning fuzzy weights to user sessions and associated URLs has been proposed. Our results clearly indicate that quality of user session clusters formed using FCLMdn algorithm is much better than those using FCM and FCMdd algorithms in terms of various cluster validity indices. Show more

Keywords: Fuzzy clustering, Fuzzy c-Least Medians clustering, web usage mining

DOI: 10.3233/IDA-150489

Citation: Intelligent Data Analysis, vol. 21, no. 3, pp. 553-575, 2017

Authors: Moshki, Mohsen | Kabiri, Peyman | Mohebalhojeh, Alireza

Article Type: Research Article

Abstract: In this paper, a new data-driven method for short-range forecasting of spatio-temporal systems is proposed. It uses NCEP data as raw data to construct forecasting model. The global model consists of several local models. Each local model is constructed in three steps. In the first step, a local dataset is constructed based on NCEP raw data. This dataset is a very high-dimensional data with huge number of redundant and irrelevant features. In the second step, a feature selection method named GRASP is applied on the local dataset and produces a new local dataset whose features are reduced significantly. In the …third step, a regression ensemble method called Bagging is used to construct a local model. Both GRASP and Bagging methods are scalable modules with respect to the computational power needed. The proposed method makes it possible to control the trade-off between speed and precision. In addition to the scalability, the proposed method, in some points produces forecasts more precise than the GFS system. Show more

Keywords: Feature selection, regression ensemble, spatio-temporal modeling, data driven modeling, Numerical Weather Prediction

DOI: 10.3233/IDA-150494

Citation: Intelligent Data Analysis, vol. 21, no. 3, pp. 577-595, 2017

Authors: Huang, Jheng-Nan | Hong, Tzung-Pei | Chiang, Ming-Chao

Article Type: Research Article

Abstract: In data mining, finding frequent itemsets is a critical step to discovering association rules. The number of frequent itemsets may, however, be huge if the threshold of minimum support is set at a low value or the number of items in the transaction database to be mined is large. In the past, some approaches were thus proposed to keep frequent itemsets with compact representation. For example, the approach of maximal itemsets keeps a borderline composed of the maximal itemsets, which separate frequent itemsets from non-frequent ones. It can recover all the frequent itemsets, but cannot get their actual frequencies back. …On the contrary, the approach of closed itemsets can correctly recover each frequent itemset and its frequency. Besides, another approach called reference itemsets can recover each frequent itemset and approximately estimate its frequency. In this paper, we propose an efficient algorithm to recover each frequent itemset and its approximate frequency based on the kept maximal itemsets, frequent 1-itemsets, their supports, and some key information. The maximal frequent itemsets are used to recover all frequent itemsets, which are then organized into a simple flow network with levels. Next, the kept key information is used to derive approximate supports of the frequent itemsets in the flow network through the flow process. Finally, a series of experiments are conducted to show the compression effects of the proposed algorithm. Show more

Keywords: Approximate support, data mining, frequent itemset, maximal itemset, flow network

DOI: 10.3233/IDA-150488

Citation: Intelligent Data Analysis, vol. 21, no. 3, pp. 597-616, 2017

Authors: Huang, Chao | Yang, Qingyu | Du, Mingwei | Yang, Donghui

Article Type: Research Article

Abstract: Financial distress prediction (FDP) has received considerable attention from both practitioners and researchers. This paper proposes a novel support vector machine (SVM) classifier ensemble framework based on earnings manipulation and fuzzy integral for FDP named SEMFI. We use financial data from the previous three years to predict companies’ current financial situation and divide the companies in each year into different categories according to whether they manipulate the earnings. Then, SVM is trained on different categories. The outputs of SVM are combined by fuzzy integral which adopts a new fuzzy measure determination method. This method considers the fact that recent financial …data are more valuable for FDP. Additionally, when using the model trained by historical data for FDP, the external environment may have dramatically changed. Therefore, a fuzzy measure dynamic adjustment method is proposed by considering the confidence of each single classifier’s output, the consistency between each single classifier’s output and the diversity among classifiers. To verify the performance of SEMFI, an empirical study using real financial data is conducted. The results indicate that the introduction of earnings manipulation, the new fuzzy measure determination and dynamic adjustment method to FDP can significantly enhance the prediction performance. Show more

Keywords: Financial distress prediction, support vector machine, classifiers ensemble, earnings manipulation, fuzzy integral

DOI: 10.3233/IDA-160034

Citation: Intelligent Data Analysis, vol. 21, no. 3, pp. 617-636, 2017

Authors: Oliveira, Claudiane Maria | Pereira, Denilson Alves

Article Type: Research Article

Abstract: Price comparison services are widely used by e-shopping customers. Such e-shopping sites receive product offers from thousands of online stores, and in order to provide price comparison, product categorization, and searching, it is necessary to match different offers referring to the same real-world product. This is a hard task, since they need to classify millions of product offers in thousands of classes, and distinct descriptions may exist for the same product, as well as very similar descriptions of distinct products. In this work, we propose a method that uses association rules to classify product offers from e-shopping web sites matching …offers against offers without the need for a product catalog. This is a supervised learning method that trains a classifier, whose generated model comprises a set of association rules to identify product offer classes. Experimental evaluations show that our method is effective and efficient, and obtains better results than three baselines in several datasets with distinct characteristics. It is able to deal with large datasets containing thousands of classes and different types of products such as electronics and books. Moreover, we propose and evaluate strategies to reduce its execution time and we evaluate its weaknesses. Show more

Keywords: Association rule, entity resolution, product classification, product matching, product offer, e-commerce

DOI: 10.3233/IDA-150444

Citation: Intelligent Data Analysis, vol. 21, no. 3, pp. 637-660, 2017