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Enhanced Feature Selection via Hierarchical Concept Modeling
(2024-12-01)
Saelee, Jarunee
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Wetchapram, Patsita
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Wanichsombat, Apirat
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Intarasit, Arthit
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Muangprathub, Jirapond
The objectives of feature selection include simplifying modeling and making the results more understandable, improving data mining efficiency, and providing clean and understandable data preparation. With big data, it also allows us to reduce computational time, improve prediction performance, and better understand the data in machine learning or pattern recognition applications. In this study, we present a new feature selection approach based on hierarchical concept models using formal concept analysis (FCA) and a decision tree (DT) for selecting a subset of attributes. The presented methods are evaluated based on all learned attributes with 10 datasets from the UCI Machine Learning Repository by using three classification algorithms, namely decision trees, support vector machines (SVM), and artificial neural networks (ANN). The hierarchical concept model is built from a dataset, and it is selected by top-down considering features (attributes) node for each level of structure. Moreover, this study is considered to provide a mathematical feature selection approach with optimization based on a paired-samples t-test. To compare the identified models in order to evaluate feature selection effects, the indicators used were information gain (IG) and chi-squared (CS), while both forward selection (FS) and backward elimination (BS) were tested with the datasets to assess whether the presented model was effective in reducing the number of features used. The results show clearly that the proposed models when using DT or using FCA, needed fewer features than the other methods for similar classification performance.
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Portfolio Risk and Return with a New Simple Moving Average of Price Change Ratio
(2020-12-01)
Muangprathub, Jirapond
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Intarasit, Arthit
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Boongasame, Laor
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Phaphoom, Nattakarn
Cluster analysis is a commonly used technique by investors to create a diversified portfolio. The approach aims at maximizing returns for a tolerable degree of risks. To diversify effectively, investors use similarity measures to enable clustering. Traditional price indexes, such as Return on Asset and Return on Equity, are known to perform inconsistently in identifying acceptable clusters. Our study proposes a novel indicator, Simple Moving Average of Price Change Ratios (SMA-PCR-N), for use as a similarity measure. It is an adjusted version of the traditional Simple Moving Average (SMA) calculated based on stocks’ closing price over a number of time periods, to observe price trend and potential changes. Instead, the SMA-PCR-N considers the daily opening prices, the closing prices and the average price of stocks. We demonstrate the use of k-means clustering with SMA-PCR-N to create a diversified stock portfolio. Data on approximately three hundred stocks were retrieved from the Stock Exchange of Thailand for the fiscal years 2015–2017, and were used experiments to evaluate the effectiveness of our SMA-PCR-N diversification approach. The results show that SMA-PCR-N based portfolios gave higher returns than portfolios created based on SMA clusters, in most cases.
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A Knowledge Integrated Case-Based Classifier
(2019-06-01)
Muangprathub, Jirapond
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Kajornkasirat, Siriwan
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Wanichsombat, Apirat
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Boonjing, Veera
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Saelee, Jarunee
This paper proposes a case-based classifier using a new approach that integrates rule-based and case-based reasoning approaches for enhanced accuracy. The rule-based reasoning component uses rules generated from a concept lattice of training data, binarized using fuzzy sets. These binarized data are stored as cases in the case-based classification component. The case-based component complements the rule-based component to enhance classification accuracy. Moreover, we designed the case-based component with an embedded similarity measure that uses a vector model for concept approximations. Thus, this design makes it possible to generate high quality rules and classify unseen new cases. In addition, the ability to build a knowledge base in lattice form is important for discovering hierarchical patterns, incrementing or updating the existing knowledge base, and inducing rules with our rule learning algorithm. The novel methodology was implemented and evaluated with benchmark datasets from the UCI repository and historic rubber prices in Thailand, demonstrating improvements in accuracy of classification calls. The results from the fact their several hierarchical datasets are very promising, with improved classification performance over prior reported methods.

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