Price Trackers Inspired by Immune Memory

Wilson, William; Birkin, Phil; Aickelin, Uwe

doi:10.1007/11823940_28

Cited by 11 publications

(9 citation statements)

References 14 publications

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“…According to the clonal selection theory, only the cells that are capable of recognizing an antigen will be proliferated. CLONALG algorithm [25], Immunity Clonal Strategy Algorithm [26], Adaptive Clonal Selection algorithm [27], CLONCLAS algorithm [28], Immunos algorithms [29,30], and Trend Evaluation Algorithm [31] are typical examples of clonal selection theory based algorithms. Negative selection is the process of eliminating T cells that recognize self-cells molecules and antigens.…”

Section: Artificial Immune Systemsmentioning

confidence: 99%

Artificial Immune System Based Web Page Classification

Onan

2015

Software Engineering in Intelligent Systems

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Abstract. Automated classification of web pages is an important research direction in web mining, which aims to construct a classification model that can classify new instances based on labeled web documents. Machine learning algorithms are adapted to textual classification problems, including web document classification. Artificial immune systems are a branch of computational intelligence inspired by biological immune systems which is utilized to solve a variety of computational problems, including classification. This paper examines the effectiveness and suitability of artificial immune system based approaches for web page classification. Hence, two artificial immune system based classification algorithms, namely Immunos-1 and Immunos-99 algorithms are compared to two standard machine learning techniques, namely C4.5 decision tree classifier and Naïve Bayes classification. The algorithms are experimentally evaluated on 50 data sets obtained from DMOZ (Open Directory Project). The experimental results indicate that artificial immune based systems achieve higher predictive performance for web page classification.

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Section: Artificial Immune Systemsmentioning

confidence: 99%

Artificial Immune System Based Web Page Classification

Onan

2015

Software Engineering in Intelligent Systems

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“…It evolves a population of solution candidates in an attempt to match part of a novel pattern, it then mutates these successful population members so that matching solutions can be improved. More information regarding the inspiration behind the MTA can be found in [20].…”

Section: Long and Short Term Memorymentioning

confidence: 99%

Motif detection inspired by immune memory

Wilson

Birkin

Aickelin

2011

Journal of the Operational Research Society

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Abstract. The search for patterns or motifs in data represents an area of key interest to many researchers. In this paper we present the Motif Tracking Algorithm, a novel immune inspired pattern identification tool that is able to identify variable length unknown motifs which repeat within time series data. The algorithm searches from a neutral perspective that is independent of the data being analysed and the underlying motifs. In this paper we test the flexibility of the motif tracking algorithm by applying it to the search for patterns in two industrial data sets. The algorithm is able to identify a population of meaningful motifs in both cases, and the value of these motifs is discussed.

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“…The parameters required in the MTA include the length of a symbol s, the match threshold r, and the alphabet size a. [20] the algorithm investigated motifs through consideration of each data point individually, creating a solution that was not scalable to larger data sets. In the MTA this problem is resolved as we investigate motifs by combining individual data points into sequences and comparing and combining those sequences to form motifs.…”

Section: Long and Short Term Memorymentioning

confidence: 99%

“…However, the use of Immune System inspired (IS) techniques in this field has remained fairly limited. In our previous work [20] an IS approach was proposed to identify patterns embedded in price data using a population of trackers that evolve using proliferation and mutation. This early research proved successful on small data sets but suffered when scaled to larger data sets with more complex motifs.…”

Section: Introductionmentioning

confidence: 99%