Siti Sakira Kamaruddin

Enhanced artificial bee colony for training least squares support vector machines in commodity price forecasting

Abstract The importance of optimizing machine learning control parameters has motivated researchers to investigate for proficient optimization techniques. In this study, a Swarm Intelligence approach, namely artificial bee colony (ABC) is utilized to optimize parameters of least squares support vector machines. Considering critical issues such as enriching the searching strategy and preventing over fitting, two modifications to the original ABC are introduced. By using commodities prices time series as empirical data, the proposed technique is compared against two techniques, including Back Propagation Neural Network and by Genetic Algorithm. Empirical results show the capability of the proposed technique in producing higher prediction accuracy for the prices of interested time series data.

Enterprise Information Architecture (EIA): Assessment of Current Practices in Malaysian Organizations

In this paper we described the findings based on a research study on current enterprise information architecture (EIA) practices in Malaysian organizations. Ten organizations from public and private sectors were chosen for case study analysis. The Zachman framework was chosen as a guideline to assess the current practice of EIA in these organizations. This study had successfully investigated the current practice and conditions of EIA in selected public and private organizations in Malaysia. The study found that majority of the organizations do practice some kind of enterprise information architecture either in-house or outsource to third parties. The study also found that certain aspects of the framework were not addressed at all, whilst other aspects that were addressed vary in terms of the different perspectives. This gives a general outlook of EIA implementation in the selected organizations, which could be incomplete or not adequately addressed. The study revealed a poor knowledge and understanding of EIA among the organizations though there had been efforts at implementing EIA focusing on the data, function and network architectures. The study discovered gaps in the current practice and provides suggestions for organizations to consciously embark on the EIA paradigm in order to better align the whole organization to its goals. Results of this study can be used by the government and private sectors to formulate new policies and guidelines on enterprise architecture so that the enterprises IT adoption and information requirements fit nicely into its business strategy

Forecasting Model Based on LSSVM and ABC for Natural Resource Commodity

 Abstract—Reliable forecasts of the price of natural resource commodity is of interest for a wide range of applications. This includes generating macroeconomic projections and in assessing macroeconomic risks. Various approaches have been introduced in developing the required forecasting models. In this paper, a forecasting model based on an optimized Least Squares Support Vector Machine is proposed. The determination of hyper-parameters is performed using a nature inspired algorithm, Artificial Bee Colony. The proposed forecasting model is realized in gold price forecasting. The undertaken experiments showed that the prediction accuracy and Mean Absolute Percentage Error produced by the proposed model is better compared to the one produced using Least Squares Support Vector Machine as an individual.

Conceptual graph formalism for financial text representation

We present an approach to automatically transform a financial text into conceptual graph formalism. The approach exploits the constituent structure of sentences and general English grammar rules to perform the transformation. We suggest face validation and traces as the evaluation method to be performed on the resulting formalism to validate its accuracy. We also discuss the potential manipulation and application of the constructed conceptual graph database.

Application of LSSVM by ABC in energy commodity price forecasting

The importance of the hyper parameters selection for a kernel-based algorithm, viz. Least Squares Support Vector Machines (LSSVM) has been a critical concern in literature. In order to meet the requirement, this work utilizes a variant of Artificial Bee Colony (known as mABC) for hyper parameters selection of LSSVM. The mABC contributes in the exploitation process of the artificial bees and is based on Levy mutation. Realized in crude oil price forecasting, the performance of mABC-LSSVM is guided based on Mean Absolute Percentage Error (MAPE) and Root Mean Square Error (RMSPE) and compared against the standard ABC-LSSVM and LSSVM optimized by Genetic Algorithm. Empirical results suggested that the mABC-LSSVM is superior than the chosen benchmark algorithms.

Automatic extraction of performance indicators from financial statements

We present a method to automatically analyze financial statements for the purpose of recognizing and extracting relevant financial indicators together with its values and its related narratives. We employ a rule-based approach to solve the problem of syntactical and morphological variations contained within the financial text. The information was extracted using a multi-pass scan to process the text in a series of pre-programmed functions. Experiments were carried out to demonstrate the feasibility of the system by deriving the precision and recall scores. The extracted results can be used to construct and augment knowledge bases for a more complex text mining systems.

Dissimilarity algorithm on conceptual graphs to mine text outliers

The graphical text representation method such as Conceptual Graphs (CGs) attempts to capture the structure and semantics of documents. As such, they are the preferred text representation approach for a wide range of problems namely in natural language processing, information retrieval and text mining. In a number of these applications, it is necessary to measure the dissimilarity (or similarity) between knowledge represented in the CGs. In this paper, we would like to present a dissimilarity algorithm to detect outliers from a collection of text represented with Conceptual Graph Interchange Format (CGIF). In order to avoid the NP-complete problem of graph matching algorithm, we introduce the use of a standard CG in the dissimilarity computation. We evaluate our method in the context of analyzing real world financial statements for identifying outlying performance indicators. For evaluation purposes, we compare the proposed dissimilarity function with a dice-coefficient similarity function used in a related previous work. Experimental results indicate that our method outperforms the existing method and correlates better to human judgements. In Comparison to other text outlier detection method, this approach managed to capture the semantics of documents through the use of CGs and is convenient to detect outliers through a simple dissimilarity function. Furthermore, our proposed algorithm retains a linear complexity with the increasing number of CGs.

Conceptual Graph Interchange Format for Mining Financial Statements

This paper addresses the automatic transformation of financial statements into conceptual graph interchange format (CGIF). The method mainly involves extracting relevant financial performance indicators, parsing it to obtain syntactic sentence structure and to generate the CGIF for the extracted text. The required components for the transformation are detailed out with an illustrative example. The paper also discusses the potential manipulation of the resulting CGIF for knowledge discovery and more precisely for deviation detection.

Short Term Traffic Forecasting Based on Hybrid of Firefly Algorithm and Least Squares Support Vector Machine

The goal of an active traffic management is to manage congestion based on current and predicted traffic conditions. This can be achieved by utilizing traffic historical data to forecast the traffic flow which later supports travellers for a better journey planning. In this study, a new method that integrates Firefly algorithm (FA) with Least Squares Support Vector Machine (LSSVM) is proposed for short term traffic speed forecasting, which is later termed as FA-LSSVM. In particular, the Firefly algorithm which has the advantage in global search is used to optimize the hyper-parameters of LSSVM for efficient data training. Experimental result indicates that the proposed FA-LSSVM generates lower error rate and a higher accuracy compared to a non-optimized LSSVM. Such a scenario indicates that FA-LSSVM would be a competitor method in the area of time series forecasting.

Deviation detection in text using conceptual graph interchange format and error tolerance dissimilarity function

The rapid increase in the amount of textual data has brought forward a growing research interest towards mining text to detect deviations. Specialized methods for specific domains have emerged to satisfy various needs in discovering rare patterns in text. This paper focuses on a graph-based approach for text representation and presents a novel error tolerance dissimilarity algorithm for deviation detection. We resolve two non-trivial problems, i.e. semantic representation of text and the complexity of graph matching. We employ conceptual graphs interchange format CGIF --a knowledge representation formalism to capture the structure and semantics of sentences. We propose a novel error tolerance dissimilarity algorithm to detect deviations in the CGIFs. We evaluate our method in the context of analyzing real world financial statements for identifying deviating performance indicators. We show that our method performs better when compared with two related text based graph similarity measuring methods. Our proposed method has managed to identify deviating sentences and it strongly correlates with expert judgments. Furthermore, it offers error tolerance matching of CGIFs and retains a linear complexity with the increasing number of CGIFs.