I'm making an application to find the best products to buy based on several criteria. can be called a decision support system.
some examples of the criteria I use are:
location, the more the sending location is with my city, the better.
I have determined the weight of the location, I determine the weight
of my city with a value of 100, the farther the shipping city with
my city, then the weight will be smaller.
the number of reviews owned by a product, more means better
rating value, the higher the rating, the more means better
price, the cheaper the price the better
I was recommended to use a method called AHP, I have read about AHP and although I think AHP is a good method, in my opinion what I want can not be fulfilled entirely with AHP because it does not take into account the nominal value of the rating and price, it only counts one thing importance to another
my questions are :
with the requirements of the criteria, what MCDM method should I use?
Does AHP actually can accommodate my needs? if yes, how? is it using Fuzzy-AHP? if so, I will start learning Fuzzy and things related to it
Thanks for the question. So, AHP*1 is a method used in decision-making (DM) to methodically assign weights to the different criterion. In order to score, rank and select the most-desirable alternative you need to complement AHP with another MCDC method that fulfils those tasks.
There several methods to do that. TOPSIS and ELECTRE, for instance, are commonly used to that purpose. *2-3. I leave you a link on the papers and tutorials of those methods so you understand how they work. -- SEE RESOURCES.
In regards to using fuzzy logic in AHP. While there are several proposals on using FAHP*4, Saaty himself, creator of the AHP states that this is redundant*5-7 since the scale in which criteria are assessed to weighing in AHP already operates with a fuzzy logic.
However, in the case, your criteria are based on qualitative data and therefore you are dealing with uncertainty and potentially, incomplete information, you can use fuzzy numbers in TOPSIS for those variables. You can check the tutorials in resources to understand how to apply those methods.
In recent years, some researchers have argued that fuzzy TOPSIS only considers the membership function. (That is, the closest an imprecise parameter is to reality) and ignores the non-membership and indeterminacy degree *9-10, so how false and not determinable is that parameter. The neutrosophic theory was mainly pioneered by *10 Smarandache.
So, in response, nowadays, neutrosophic TOPSIS is being to be used to deal with uncertainty. I recommend reading the papers below to understand the concept.
So, in summary, I will personally recommend applying AHP and Fuzzy or Neutrosophic TOPSIS to address your problem.
Resources:
Manoj Mathew. Tutorial Youtube FAHP. Fuzzy Analytic Hierarchy Process (FAHP) - Using Geometric Mean. Retrieved from: https://www.youtube.com/watch?v=5k3Wz1AfVWs
Manoj Mathew. Tutorial Youtube FTOPSIS. Fuzzy TOPSIS. Retrieved from: https://www.youtube.com/watch?v=z188EQuWOGU
Manoj Mathew. TOPSIS - Technique for Order Preference by Similarity to Ideal Solution Retrieved from: https://www.youtube.com/watch?v=kfcN7MuYVeI
MCDC in R: https://www.rdocumentation.org/packages/MCDA/versions/0.0.19
MCDC in JS: https://www.npmjs.com/package/electre-js
MCDC in Python: https://github.com/pyAHP/pyAHP
REFERENCES:
1 Saaty, R. W. (1987). The analytic hierarchy process—what it is and how it is used. Mathematical Modelling, 9(3-5), 167.
doi:10.1016/0270-0255(87)90473-8
2 Hwang, C. L., & Yoon, K. (1981). Methods for multiple attribute decision making. In Multiple attribute decision making (pp. 58-191). Springer, Berlin, Heidelberg.
3 Figueira, J., Mousseau, V., & Roy, B. (2005). ELECTRE methods. In Multiple criteria decision analysis: State of the art surveys (pp. 133-153). Springer, New York, NY.
4 Mardani, A., Nilashi, M., Zavadskas, E. K., Awang, S. R., Zare, H., & Jamal, N. M. (2018). Decision Making Methods Based on Fuzzy Aggregation Operators: Three Decades Review from 1986 to 2017.
International Journal of Information Technology & Decision Making, 17(02), 391–466. doi:10.1142/s021962201830001x
5 Saaty, T. L. (1986). Axiomatic Foundation of the Analytic Hierarchy Process. Management Science, 32(7), 841.
doi:10.1287/mnsc.32.7.841
6 Saaty, R. W. (1987). The analytic hierarchy process—what it is and how it is used. Mathematical Modelling, 9(3-5), 167.
doi:10.1016/0270-0255(87)90473-8
7 Aczél, J., & Saaty, T. L. (1983). Procedures for synthesizing ratio judgements. Journal of Mathematical Psychology, 27(1), 93–102. doi:10.1016/0022-2496(83)90028-7
8 Wang, Y. M., & Elhag, T. M. (2006). Fuzzy TOPSIS method based on alpha level sets with an application to bridge risk assessment. Expert systems with applications, 31(2), 309-319
9 Zhang, Z., Wu, C.: A novel method for single-valued neutrosophic multi-criteria decision making with incomplete weight information. Neutrosophic Sets Syst. 4, 35–49 (2014)
10 Biswas, P., Pramanik, S., & Giri, B. C. (2018). Neutrosophic TOPSIS with Group Decision Making. Studies in Fuzziness and Soft Computing, 543–585. doi:10.1007/978-3-030-00045-5_21
10 Smarandache, F.: A Unifying Field in Logics. Neutrosophy: Neutrosophic Probability, Setand Logic. American Research Press, Rehoboth (1998)
Related
I am wondering if is it possible to create a global score using multiple KPIs with different scales.
Example:
I would like to join all this KPIs in one score that could tell me what version is better. Is it possible? (I consider the 3 with the same weight in the score)
There is quite some theory on (credit) rating methods which provides a profound mathematical base for what you are after. You might start reading about score cards in general. An abundant way of combining different scores uses Logit.
The short answer to your question: There is no single best way to combine three KPIs, you have to try different formulas, and decide on one of the formulas based on some statistics tests in a validation step.
Further reading
Using a Balanced Scorecard to Measure Your Key Performance Indicators - a brief primer on the topic
Chapter on Logit from the book Stefan Trueck, Svetlozar T. Rachev: Rating Based Modeling of Credit Risk: Theory and Application of Migration
Guidelines on Credit Risk Management - OeNB as PDF
Preface: I'm a self-taught coder, so a lot of my knowledge is limited to my research. I'm hoping to have other opinions as I want to build things right the first time. I need help with determining an appropriate solution and how to implement the solution.
I'm looking to build a least cost alternative model (essentially a shortest path) for delivering between locations (nodes), based on different modes of transportation (vehicles) and the different roads taken (paths). Another consideration is the product price (value) to determine the least cost path.
Here are my important data items:
nodes: cities where the product will travel to and from.
paths: roads have different costs, depending on the road.
vehicles: varying vehicles have differing rental costs when transporting (motorbike, car, truck). Note that the cost of a vehicle is not constant, it is highly dependent on the to/from nodes. For example, using a car to go from city A to city B will have a different cost than using a car to go from city B to A or city A to city C.
value: Product value. Again, a product's value is highly dependent on its destination node. The same product can have a different value at City A, B or C.
Problem Statement
How to setup data structure to best determine where the least cost path would be to get a product from one location to every other location.
Possible Solutions
From my research, I believe a weighted graph data structure would be most suitable for my situation in combination with dijkstra's algorithm. I believe breaking the problem down simpler would be essential, to first create a simple weighted graph of only nodes and paths.
From there, adding the vehicle cost and the product value considerations afterwards. Perhaps just adding the two values as a cost to "visit" a node? (aka incorporate it into the path cost?)
Thoughts on my current solution? Other considerations I overlooked? Perhaps a better solution?
Implementation
I'd love to be able to build this within Excel VBA (as that is how I learned how to code) and Excel is what I use for my tools. Would VBA be too limited in this task? How else can I incorporate my analysis with Excel with another language?
Try the book Practical Management Science by Winston & Albright and check out the chapter on Operations Management - lots of models explained in there from the simple onwards. Available online as a pdf : http://ingenieria-industrial.net/downloads/practicalmanagementscience.pdf
VBA is more a scripting language than a full-fledged one, though one may contend that the underlying framework is .NET. Why don't you give a shot at C++ or Java? If you intuitively understand the data structure and the algorithm, then it'll be a breeze coding in these. Chapter 4 of Algorithms by Sedgewick and Wayne has a beautiful explanation of Shortest Paths. You may also consider studying Bellman-Ford algorithm if you foresee any negative weight cycles on a vertex.
Is there something a [directional?] notion/implementation of distance between Wikipedia categories/pages?
For example consider: A) "Saint Louis University" B) "university"
Clearly "A" is a type of "B". How can you extract this from Wiki?
If you extract all the categories connect to A, you'd see that it gives
Category:1818 establishments in Missouri Territory
Category:Articles containing Latin-language text
Category:Association of Catholic Colleges and Universities
Category:Commons category with local link same as on Wikidata
Category:Coordinates on Wikidata
Category:Educational institutions established in 1818
Category:Instances of Infobox university using image size
Category:Jesuit universities and colleges in the United States
Category:Roman Catholic Archdiocese of St. Louis
Category:Roman Catholic universities and colleges in Missouri
and it does not contain anything that would directly connect to B (https://en.wikipedia.org/wiki/University). But essentially if you look further, you should be able to find a multi-hop path between A and B, possibly multiple hops. What are the popular ways of accomplishing this?
Some ideas/resources I collected. Will update this if I find more.
-- Using DBPedia: knowledge base curated based on Wiki. They provide an SparQL end-point to query this KB. But one has to simulate the desired similarity/distance behavior via their SparQL interface. Some ideas are here and here, but they seem to be outdated.
-- Using UMBEL: http://umbel.org/ which is a knowledge graph of concepts. I think the size of this knowledge graph is relatively small. But the I suspect that its precision is probably high. That being said, I'm not sure how this relates to Wikipedia at all. They have this api for calculating the distance measure between any pair of their concepts (at the moment of writing this post, their similarity API is down. So not a feasible solution at the moment).
-- Using http://degreesofwikipedia.com/ I don't the details of their algorithm and how they do, but they provide a distance between Wiki-concepts. And also this is directional. For example this and this.
If you have the entire Wikipedia category taxonomy, then you can compute the distance (shortest path length) between two categories. If one category is the ancestor of other, it is straight forward.
Otherwise you can find the Least Common Subsumer which is defined as follows.
Least common subsumer of two concepts A and B is the most specific
concept which is an ancestor of both A and B.
Then compute the distance between them via LCS.
I encourage you to go through similarity measures where you will find state-of-art techniques to compute semantic similarity between words.
Resource: My project on extracting Wikipedia category/concept might help you.
One very good related example
Compute semantic similarity between words using WordNet. WordNet organizes English words in hierarchical fashion. See this wordnet similarity for java demo. It uses eight different state-of-techniques to compute semantic similarity between words.
You might be looking for the "is a" relationship: Q734774 (the Wikidata item for Saint Louis University) is a university, a building and a private not-for-profit educational institution. You can use SPARQL to query it:
is Saint Louis University a university?
how far is Saint Louis University removed from the concept of "university"? (although I doubt this would produce anything meaningful)
I've been working on a small, personal project which takes a user's job skills and suggests the most ideal career for them based on those skills. I use a database of job listings to achieve this. At the moment, the code works as follows:
1) Process the text of each job listing to extract skills that are mentioned in the listing
2) For each career (e.g. "Data Analyst"), combine the processed text of the job listings for that career into one document
3) Calculate the TF-IDF of each skill within the career documents
After this, I'm not sure which method I should use to rank careers based on a list of a user's skills. The most popular method that I've seen would be to treat the user's skills as a document as well, then to calculate the TF-IDF for the skill document, and use something like cosine similarity to calculate the similarity between the skill document and each career document.
This doesn't seem like the ideal solution to me, since cosine similarity is best used when comparing two documents of the same format. For that matter, TF-IDF doesn't seem like the appropriate metric to apply to the user's skill list at all. For instance, if a user adds additional skills to their list, the TF for each skill will drop. In reality, I don't care what the frequency of the skills are in the user's skills list -- I just care that they have those skills (and maybe how well they know those skills).
It seems like a better metric would be to do the following:
1) For each skill that the user has, calculate the TF-IDF of that skill in the career documents
2) For each career, sum the TF-IDF results for all of the user's skill
3) Rank career based on the above sum
Am I thinking along the right lines here? If so, are there any algorithms that work along these lines, but are more sophisticated than a simple sum? Thanks for the help!
The second approach you explained will work. But there are better ways to solve this kind of problem.
At first you should know a little bit about language models and leave the vector space model.
In the second step based on your kind of problem that is similar to expert finding/profiling you should learn a baseline language model framework to implement a solution.
You can implement A language modeling framework for expert finding with a little changes so that the formulas can be adapted to your problem.
Also reading On the assessment of expertise profiles will give you a better understanding of expert profiling with the framework above.
you can find some good ideas, resources and projects on expert finding/profiling at Balog's blog.
I would take SSRM [1] approach to expand query (job documents) using WordNet (extracted database [2]) as semantic lexicon - so you are not constrained only to direct word-vs-word matches. SSRM has its own similarity measure (I believe the paper is open-access, if not, check this: http://blog.veles.rs/document-similarity-computation-models-literature-review/, there are many similarity computation models listed). Alternativly, and if your corpus is big enough, you might try LSA/LSI[3,4] (also covered on the page) - without using external lexicon. But, if it is on English, WordNet's semantic graph is really rich in all directions (hyponims, synonims, hypernims... concepts/SinSet).
The bottom line: I would avoid simple SVM/TF-IDF for such concrete domain. I measured really serious margin of SSRM, over TF-IDF/VSM (measured as macro-average F1, 5-class single label classification, narrow domain).
[1] A. Hliaoutakis, G. Varelas, E. Voutsakis, E.G.M. Petrakis, E. Milios, Information Retrieval by Semantic Similarity, Int. J. Semant. Web Inf. Syst. 2 (2006) 55–73. doi:10.4018/jswis.2006070104.
[2] J.E. Petralba, An extracted database content from WordNet for Natural Language Processing and Word Games, in: 2014 Int. Conf. Asian Lang. Process., 2014: pp. 199–202. doi:10.1109/IALP.2014.6973502.
[3] P.W. Foltz, Latent semantic analysis for text-based research, Behav. Res. Methods, Instruments, Comput. 28 (1996) 197–202. doi:10.3758/BF03204765.
[4] A. Kashyap, L. Han, R. Yus, J. Sleeman, T. Satyapanich, S. Gandhi, T. Finin, Robust semantic text similarity using LSA, machine learning, and linguistic resources, Springer Netherlands, 2016. doi:10.1007/s10579-015-9319-2.
I wanted to understand the puropse of using SNOMED-CT for normalization of clinical terms.
Let's say I have a criteria/statement like
Gender is Male
My question is if SNOMED-CT is used for normalizing both
Gender and Male OR just one of them like
Sex is M OR
Gender is M
I'm not sure I quite follow the question but this might help. SNOMED CT can repressent the same information in multiple ways. For example left sided hip scan can be repressented using a single concept (426100003 | Ultrasound scan of left hip |) or gluing a laterality of left to the concept for ultrasound of hip (the actual expression is a little complex here, I can post it if you need).
However when doing some operations, e.g. subsumption tests, the form needs to be consistent. Thus there is are standardised forms and standard algorithms to get to them, I nearly always use the Long Normal Form.
So in short the normal form of an expression is a standard repressentation of that expression which can be transformed to from other repressentations.
More information can be found if you search "Normal form" on the technical reference guide: http://ihtsdo.org/fileadmin/user_upload/doc/en_gb/tig.html
Both. It includes terms for the abstract concept of "Gender", the notion of a "Finding of biological sex", and the concept of a specific finding like "Male":
http://browser.ihtsdotools.org/?perspective=full&conceptId1=365873007
http://browser.ihtsdotools.org/?perspective=full&conceptId1=429019009
http://browser.ihtsdotools.org/?perspective=full&conceptId1=248153007
However, please note that the concept of Gender is different from Sex.
Supporting the answer above but from a different perspective
Normalization using SNOMED CT allows computer to
- Define a single set of representations (i.e. you don't have to map from M or F) that can be used for information exchange and understood in all healthcare settings irrespective of the geographic or healthcare domain.
- These representations are used as rules for queries in clinical decision support (for example). Where these rules are developed by a professional body (such as e.g. pharmacists) the rules can be shared irrespective of your legacy system and used consistently across all products. At least that is the intention.
This supports safe clinical practice.